A Survey on Causal Discovery: Theory and Practice
Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is specifically designed to quantify the underlying relationships that co...
Uloženo v:
| Vydáno v: | International journal of approximate reasoning Ročník 151; s. 101 - 129 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Inc
01.12.2022
|
| Témata: | |
| ISSN: | 0888-613X, 1873-4731 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is specifically designed to quantify the underlying relationships that connect a cause to its effect. Causal discovery is a branch of the broader field of causality in which causal graphs are recovered from data (whenever possible), enabling the identification and estimation of causal effects. In this paper, we explore recent advancements in causal discovery in a unified manner, provide a consistent overview of existing algorithms developed under different settings, report useful tools and data, present real-world applications to understand why and how these methods can be fruitfully exploited. |
|---|---|
| AbstractList | Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is specifically designed to quantify the underlying relationships that connect a cause to its effect. Causal discovery is a branch of the broader field of causality in which causal graphs are recovered from data (whenever possible), enabling the identification and estimation of causal effects. In this paper, we explore recent advancements in causal discovery in a unified manner, provide a consistent overview of existing algorithms developed under different settings, report useful tools and data, present real-world applications to understand why and how these methods can be fruitfully exploited. |
| Author | Zanga, Alessio Stella, Fabio Ozkirimli, Elif |
| Author_xml | – sequence: 1 givenname: Alessio orcidid: 0000-0003-4423-2121 surname: Zanga fullname: Zanga, Alessio email: alessio.zanga@unimib.it organization: Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca, 336, 20126 Milano, Italy – sequence: 2 givenname: Elif surname: Ozkirimli fullname: Ozkirimli, Elif email: elif.ozkirimli@roche.com organization: Data and Analytics Chapter, F. Hoffmann - La Roche Ltd, Basel, Switzerland – sequence: 3 givenname: Fabio surname: Stella fullname: Stella, Fabio email: fabio.stella@unimib.it organization: Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca, 336, 20126 Milano, Italy |
| BookMark | eNp9z09LwzAYx_EgE9ymb8BT30Drk6RLUvEy5l8YKDjBW3iaPsWU2UrSDfru7ZgnDzv9Tp8ffGds0nYtMXbNIePA1U2T-QZDJkCIDIoMID9jU260THMt-YRNwRiTKi4_L9gsxgYAlM7NlPFl8r4LexqSrk1WuIu4Te59dN2ewnCbbL6oC0OCbZW8BXS9d3TJzmvcRrr62zn7eHzYrJ7T9evTy2q5Tp0E6FMsJfFcKKN0qQmUEGWNuSTDq6LQupClwRx5rZWrylwKUo67AhaIi8rUIOWcmeOvC12MgWrrfI-979o-oN9aDvaQbht7SLeHdAuFHdNHKv7Rn-C_MQyn0d0R0Ri19xRsdJ5aR5UP5Hpbdf4U_wXxqHO9 |
| CitedBy_id | crossref_primary_10_1186_s12911_024_02837_0 crossref_primary_10_1186_s12911_025_02981_1 crossref_primary_10_1016_j_jbi_2025_104877 crossref_primary_10_1093_bib_bbae521 crossref_primary_10_3390_s24123728 crossref_primary_10_1109_TVCG_2024_3456346 crossref_primary_10_1103_PhysRevE_112_014204 crossref_primary_10_1016_j_neucom_2024_128701 crossref_primary_10_1016_j_scitotenv_2025_180121 crossref_primary_10_1109_ACCESS_2024_3451626 crossref_primary_10_3390_e27050531 crossref_primary_10_1016_j_ins_2025_122240 crossref_primary_10_3389_fpubh_2024_1305746 crossref_primary_10_1016_j_compchemeng_2025_109345 crossref_primary_10_1007_s13748_024_00348_7 crossref_primary_10_1186_s12967_025_06755_1 crossref_primary_10_1016_j_dajour_2025_100639 crossref_primary_10_1145_3629169 crossref_primary_10_1016_j_eswa_2024_126120 crossref_primary_10_1016_j_ipm_2025_104202 crossref_primary_10_3390_e26100867 crossref_primary_10_1109_TFUZZ_2024_3471187 crossref_primary_10_1007_s10462_022_10351_w crossref_primary_10_1016_j_eswa_2023_122690 crossref_primary_10_1145_3687467 crossref_primary_10_3390_e26020108 crossref_primary_10_1016_j_engappai_2024_108258 crossref_primary_10_3390_cancers16213643 crossref_primary_10_1016_j_dajour_2023_100291 crossref_primary_10_3390_e26110946 crossref_primary_10_1016_j_tre_2025_104244 crossref_primary_10_1145_3705297 crossref_primary_10_1109_TAI_2023_3329786 crossref_primary_10_1109_ACCESS_2025_3596680 crossref_primary_10_1016_j_dt_2024_04_007 crossref_primary_10_1080_10643389_2025_2557306 |
| Cites_doi | 10.2333/bhmk.41.65 10.1093/nar/gkx1013 10.1016/j.ijar.2012.09.004 10.1016/j.egyr.2021.09.026 10.1109/TAC.1974.1100705 10.1109/JPROC.2021.3058954 10.3934/jdg.2021008 10.1016/j.ijar.2019.10.003 10.18637/jss.v035.i03 10.1089/cmb.2008.09TT 10.18637/jss.v047.i11 10.1111/phc3.12470 10.1162/NECO_a_00708 10.1162/003465304323023651 10.1016/0005-1098(78)90005-5 10.1016/0165-1684(94)90029-9 10.1016/j.cell.2016.11.038 10.1016/j.ijar.2019.10.009 10.1016/S0140-6736(12)62129-1 10.1214/aos/1031689015 10.1214/17-AOS1654 10.1016/j.spl.2016.11.010 10.1198/jcgs.2010.08162 10.1186/1471-2105-7-43 10.1016/j.cell.2015.04.044 10.1007/s41060-017-0094-6 10.1111/acer.13914 10.3389/fgene.2019.00524 10.1109/TCBB.2016.2591526 10.1126/science.1105809 10.1214/aos/1031833662 10.1007/s10994-006-6889-7 10.1007/s41060-016-0032-z 10.1214/11-AOS940 10.1016/j.artint.2008.08.001 10.18637/jss.v077.i02 10.1007/s41060-018-0097-y 10.1109/TIT.2010.2060095 10.1214/14-AOS1260 10.1093/biomet/82.4.669 |
| ContentType | Journal Article |
| Copyright | 2022 Elsevier Inc. |
| Copyright_xml | – notice: 2022 Elsevier Inc. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.ijar.2022.09.004 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1873-4731 |
| EndPage | 129 |
| ExternalDocumentID | 10_1016_j_ijar_2022_09_004 S0888613X22001402 |
| GroupedDBID | --K --M .~1 0R~ 1B1 1RT 1~. 1~5 29J 4.4 457 4G. 5GY 5VS 6I. 7-5 71M 8P~ 9JN 9JO AAAKF AACTN AAEDT AAEDW AAFTH AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARIN AAXUO AAYFN ABAOU ABBOA ABFNM ABJNI ABMAC ABUCO ABVKL ABXDB ABYKQ ACAZW ACDAQ ACGFS ACNCT ACNNM ACRLP ACZNC ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEKER AENEX AEXQZ AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIGVJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ARUGR ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN GBLVA GBOLZ HAMUX HVGLF HZ~ IHE IXB J1W JJJVA KOM LG9 LY1 M41 MHUIS MO0 N9A NCXOZ O-L O9- OAUVE OK1 OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SES SEW SPC SPCBC SSB SSD SST SSV SSW SSZ T5K UHS WUQ XPP ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO ADVLN AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c300t-ab3e1426867b7e0622bfa43e81d997793b8a4a1f76cdb432e6c1c905aa5d8f033 |
| ISICitedReferencesCount | 47 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000876728600006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0888-613X |
| IngestDate | Tue Nov 18 20:50:48 EST 2025 Sat Nov 29 07:13:11 EST 2025 Fri Feb 23 02:42:08 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Causality Structural learning Causal models Causal discovery |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-ab3e1426867b7e0622bfa43e81d997793b8a4a1f76cdb432e6c1c905aa5d8f033 |
| ORCID | 0000-0003-4423-2121 |
| PageCount | 29 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_ijar_2022_09_004 crossref_primary_10_1016_j_ijar_2022_09_004 elsevier_sciencedirect_doi_10_1016_j_ijar_2022_09_004 |
| PublicationCentury | 2000 |
| PublicationDate | December 2022 2022-12-00 |
| PublicationDateYYYYMMDD | 2022-12-01 |
| PublicationDate_xml | – month: 12 year: 2022 text: December 2022 |
| PublicationDecade | 2020 |
| PublicationTitle | International journal of approximate reasoning |
| PublicationYear | 2022 |
| Publisher | Elsevier Inc |
| Publisher_xml | – name: Elsevier Inc |
| References | Rubenstein, Bongers, Mooij, Schölkopf (br0210) 2018 Scutari, Graafland, Gutiérrez (br0800) 2019; 115 Spirtes (br1150) 2001 Massmann, Gentine, Runge (br0180) 2021 Hoyer, Shimizu, Kerminen (br0460) 2006 Mooij, Magliacane, Claassen (br0280) 2020; 21 Dixit, Parnas, Li, Chen, Fulco, Jerby-Arnon, Marjanovic, Dionne, Burks, Raychowdhury (br1000) 2016; 167 Strobl, Visweswaran, Spirtes (br1220) 2018; 6 Ramsey, Zhang, Glymour, Romero, Huang, Ebert-Uphoff, Samarasinghe, Barnes, Glymour (br1160) 2018 Malinsky, Danks (br0130) 2018; 13 Eberhardt, Glymour, Scheines (br0920) 2012 Han, Cho, Lee, Yun, Kim, Bae, Yang, Kim, Lee, Kim (br0990) 2018; 46 Peters, Bühlmann (br1050) 2015; 27 Lacerda, Spirtes, Ramsey, Hoyer (br0490) 2008 Brouillard, Lachapelle, Lacoste, Lacoste-Julien, Drouin (br0580) 2020 Nagase, Kano (br0830) 2017; 122 Meek (br0310) 2013 Ogarrio, Spirtes, Ramsey (br0430) 2016 Colombo, Maathuis, Kalisch, Richardson (br1140) 2012; 40 Liu, Roeder, Wasserman (br1060) 2010; 24 Tsamardinos, Aliferis, Statnikov, Statnikov (br1110) 2003 Kalisch, Mächler, Colombo, Maathuis, Bühlmann (br1130) 2012; 47 Squires, Wang, Uhler (br0540) 2020 Nogueira, Gama, Ferreira (br0150) 2021; 8 Hu, Li, Vetta (br0930) 2014 Bongers, Mooij (br0200) 2018 Shen, Ma, Vemuri, Simon (br1190) 2020; 10 Forré, Mooij (br0330) 2017 Guo, Cheng, Li, Hahn, Liu (br0110) 2021; 53 Koller, Friedman (br0600) 2009 Richardson, Spirtes (br0350) 2002; 30 Chickering (br0670) 2002; 3 Andrews, Ramsey, Cooper (br0610) 2019 Shimizu (br0230) 2014; 41 Peters, Janzing, Schölkopf (br0370) 2017 Bühlmann, Peters, Ernest (br1090) 2014; 42 Spirtes, Zhang (br0190) 2016 Shannon (br1020) 2021 Spirtes, Meek, Richardson (br0650) 2013 Richardson (br0480) 2013 Verma, Pearl (br0270) 1990 Drton, Richardson (br0360) 2004 Tagasovska, Chavez-Demoulin, Vatter (br0450) 2020 Rantanen, Hyttinen, Järvisalo (br0570) 2020 Witte, Foraita, Didelez (br1230) 2021 Klein, Mazutis, Akartuna, Tallapragada, Veres, Li, Peshkin, Weitz, Kirschner (br0980) 2015; 161 Pearl (br0170) 2018 Spirtes (br0810) 2010; 11 Solus, Wang, Uhler (br0940) 2021 Pearl (br0260) 1995; 82 Bareinboim, Correa, Ibeling, Icard (br0080) 2022 Yu, Wu, Wang, Ding (br1260) 2010 Glymour, Pearl, Jewell (br0090) 2016 Marbach, Schaffter, Mattiussi, Floreano (br1030) 2009; 16 Hyttinen, Saikko, Järvisalo (br0500) 2017 Nogueira, Pugnana, Ruggieri, Pedreschi, Gama (br0100) 2022 Magliacane, Claassen, Mooij (br0860) 2017 Rissanen (br0730) 1978; 14 Meek (br0720) 1997 Addo, Manibialoa, McIsaac (br1180) 2021; 7 Tian, Pearl (br0900) 2013 Sachs, Perez, Pe'er, Lauffenburger, Nolan (br0970) 2005; 308 Anker, Kummerfeld, Rix, Burwell, Kushner (br1210) 2019; 43 Van den Bulcke, Van Leemput, Naudts, van Remortel, Ma, Verschoren, De Moor, Marchal (br1010) 2006; 7 Li, Fan (br0640) 2020; 12 Ahmed, Träuble, Goyal, Neitz, Bengio, Schölkopf, Wüthrich, Bauer (br0960) 2020 Scheines, Ramsey (br1040) 2016; vol. 1792 Zhang (br0340) 2008; 172 Rothenhäusler, Heinze, Peters, Meinshausen (br0560) 2015 Stegle, Janzing, Zhang, Mooij, Schölkopf (br0750) 2010; 23 Yang, Katcoff, Uhler (br0290) 2018 Comon (br0760) 1994; 36 Shahbazinia, Salehkaleybar, Hashemi (br0220) 2021 Kocaoglu, Jaber, Shanmugam, Bareinboim (br0910) 2019 Spirtes (br0840) 2013 Miley, Meyer-Kalos, Ma, Bond, Kummerfeld, Vinogradov (br1200) 2021 Shpitser, Pearl (br0880) 2008; 9 Triantafillou, Tsamardinos (br1250) 2015; 16 Geiger, Heckerman (br0700) 1994 Vowels, Camgoz, Bowden (br0140) 2021 Schwarz (br0690) 1978 Natori, Uto, Nishiyama, Kawano, Ueno (br0790) 2015 Scutari (br1120) 2017; 77 Cai, Qiao, Zhang, Zhang, Hao (br0440) 2018; 32 Alonso-Barba, Gámez, Puerta (br0400) 2013; 54 Pearl, Mackenzie (br0870) 2018 Scutari (br1100) 2010; 35 Spirtes, Glymour, Scheines, Heckerman (br0070) 2000 Hernán, Robins (br0060) 2020 Glymour, Zhang, Spirtes (br0050) 2019; 10 Bongers, Forré, Peters, Mooij (br0240) 2021 Mooij, Claassen (br0250) 2020 Nandy, Hauser, Maathuis (br0420) 2018 Psychiatric Genomics Consortium (br0020) 2013; 381 Le, Hoang, Li, Liu, Liu, Hu (br0630) 2019; 16 Gao, Chen, Shen, Liu, Gong, Bondell (br1270) 2021 Berry (br0820) 1984 Jabbari, Ramsey, Spirtes, Cooper (br1170) 2017 Tsamardinos, Brown, Aliferis (br0770) 2006; 65 Kocaoglu, Shanmugam, Bareinboim (br0320) 2017 Hauser, Bühlmann (br0530) 2012; 13 Castillo, Gutierrez, Hadi (br0590) 2012 Jaber, Kocaoglu, Shanmugam, Bareinboim (br0550) 2020 Ramsey, Glymour, Sanchez-Romero, Glymour (br0410) 2017; 3 Colombo, Maathuis (br0390) 2013 Akaike (br0680) 1974; 19 Andersson, Madigan, Perlman (br0300) 1997; 25 Hyttinen, Eberhardt, Järvisalo (br0850) 2014 Tsagris, Borboudakis, Lagani, Tsamardinos (br0620) 2018; 6 Schölkopf, Locatello, Bauer, Ke, Kalchbrenner, Goyal, Bengio (br0160) 2021; 109 Niinimaki, Parviainen (br0780) 2012 Janzing, Schölkopf (br0740) 2010; 56 Biza, Tsamardinos, Triantafillou (br1070) 2020; vol. 138 Hill (br0030) 2011; 20 Kalainathan, Goudet (br1080) 2019 Rantanen, Hyttinen, Järvisalo (br0510) 2020; 117 Scutari (br0710) 2016 Moraffah, Sheth, Karami, Bhattacharya, Wang, Tahir, Raglin, Liu (br0120) 2021 Shimizu, Blöbaum (br0380) 2020 Markowetz, Grossmann, Spang (br0890) 2005; vol. R5 Lee, Correa, Bareinboim (br0660) 2020 Forré, Mooij (br0520) 2018 Huang, Zhang, Zhang, Ramsey, Sanchez-Romero, Glymour, Schölkopf (br1240) 2020; 21 Pearl (br0040) 2018 Mooij, Peters, Janzing, Zscheischler, Schölkopf (br0950) 2016; 17 Imbens (br0010) 2004; 86 Zheng, Aragam, Ravikumar, Xing (br0470) 2018 Alonso-Barba (10.1016/j.ijar.2022.09.004_br0400) 2013; 54 Bareinboim (10.1016/j.ijar.2022.09.004_br0080) 2022 Pearl (10.1016/j.ijar.2022.09.004_br0170) 2018 Dixit (10.1016/j.ijar.2022.09.004_br1000) 2016; 167 Lee (10.1016/j.ijar.2022.09.004_br0660) 2020 Biza (10.1016/j.ijar.2022.09.004_br1070) 2020; vol. 138 Rantanen (10.1016/j.ijar.2022.09.004_br0510) 2020; 117 Spirtes (10.1016/j.ijar.2022.09.004_br0070) 2000 Zheng (10.1016/j.ijar.2022.09.004_br0470) Shimizu (10.1016/j.ijar.2022.09.004_br0230) 2014; 41 Mooij (10.1016/j.ijar.2022.09.004_br0950) 2016; 17 Shimizu (10.1016/j.ijar.2022.09.004_br0380) 2020 Jabbari (10.1016/j.ijar.2022.09.004_br1170) 2017 Massmann (10.1016/j.ijar.2022.09.004_br0180) Niinimaki (10.1016/j.ijar.2022.09.004_br0780) 2012 Scheines (10.1016/j.ijar.2022.09.004_br1040) 2016; vol. 1792 Forré (10.1016/j.ijar.2022.09.004_br0520) Akaike (10.1016/j.ijar.2022.09.004_br0680) 1974; 19 Verma (10.1016/j.ijar.2022.09.004_br0270) 1990 Pearl (10.1016/j.ijar.2022.09.004_br0870) 2018 Forré (10.1016/j.ijar.2022.09.004_br0330) Spirtes (10.1016/j.ijar.2022.09.004_br0190) 2016 Lacerda (10.1016/j.ijar.2022.09.004_br0490) 2008 Jaber (10.1016/j.ijar.2022.09.004_br0550) 2020 Bühlmann (10.1016/j.ijar.2022.09.004_br1090) 2014; 42 Malinsky (10.1016/j.ijar.2022.09.004_br0130) 2018; 13 Richardson (10.1016/j.ijar.2022.09.004_br0350) 2002; 30 Le (10.1016/j.ijar.2022.09.004_br0630) 2019; 16 Richardson (10.1016/j.ijar.2022.09.004_br0480) Spirtes (10.1016/j.ijar.2022.09.004_br0810) 2010; 11 Glymour (10.1016/j.ijar.2022.09.004_br0090) 2016 Squires (10.1016/j.ijar.2022.09.004_br0540) Chickering (10.1016/j.ijar.2022.09.004_br0670) 2002; 3 Sachs (10.1016/j.ijar.2022.09.004_br0970) 2005; 308 Bongers (10.1016/j.ijar.2022.09.004_br0200) Hyttinen (10.1016/j.ijar.2022.09.004_br0500) 2017 Bongers (10.1016/j.ijar.2022.09.004_br0240) Scutari (10.1016/j.ijar.2022.09.004_br1100) 2010; 35 Scutari (10.1016/j.ijar.2022.09.004_br0800) 2019; 115 Liu (10.1016/j.ijar.2022.09.004_br1060) 2010; 24 Ogarrio (10.1016/j.ijar.2022.09.004_br0430) 2016 Magliacane (10.1016/j.ijar.2022.09.004_br0860) Rothenhäusler (10.1016/j.ijar.2022.09.004_br0560) 2015 Natori (10.1016/j.ijar.2022.09.004_br0790) 2015 Shannon (10.1016/j.ijar.2022.09.004_br1020) 2021 Scutari (10.1016/j.ijar.2022.09.004_br0710) 2016 Cai (10.1016/j.ijar.2022.09.004_br0440) 2018; 32 Imbens (10.1016/j.ijar.2022.09.004_br0010) 2004; 86 Tagasovska (10.1016/j.ijar.2022.09.004_br0450) 2020 Tsagris (10.1016/j.ijar.2022.09.004_br0620) 2018; 6 Kalisch (10.1016/j.ijar.2022.09.004_br1130) 2012; 47 Ramsey (10.1016/j.ijar.2022.09.004_br1160) 2018 Li (10.1016/j.ijar.2022.09.004_br0640) 2020; 12 Schwarz (10.1016/j.ijar.2022.09.004_br0690) 1978 Zhang (10.1016/j.ijar.2022.09.004_br0340) 2008; 172 Witte (10.1016/j.ijar.2022.09.004_br1230) Rissanen (10.1016/j.ijar.2022.09.004_br0730) 1978; 14 Mooij (10.1016/j.ijar.2022.09.004_br0280) 2020; 21 Hernán (10.1016/j.ijar.2022.09.004_br0060) 2020 Hauser (10.1016/j.ijar.2022.09.004_br0530) 2012; 13 Meek (10.1016/j.ijar.2022.09.004_br0310) Schölkopf (10.1016/j.ijar.2022.09.004_br0160) 2021; 109 Mooij (10.1016/j.ijar.2022.09.004_br0250) 2020 Tian (10.1016/j.ijar.2022.09.004_br0900) Markowetz (10.1016/j.ijar.2022.09.004_br0890) 2005; vol. R5 Ahmed (10.1016/j.ijar.2022.09.004_br0960) Triantafillou (10.1016/j.ijar.2022.09.004_br1250) 2015; 16 Ramsey (10.1016/j.ijar.2022.09.004_br0410) 2017; 3 Spirtes (10.1016/j.ijar.2022.09.004_br0650) Pearl (10.1016/j.ijar.2022.09.004_br0260) 1995; 82 Andersson (10.1016/j.ijar.2022.09.004_br0300) 1997; 25 Spirtes (10.1016/j.ijar.2022.09.004_br0840) Huang (10.1016/j.ijar.2022.09.004_br1240) 2020; 21 Psychiatric Genomics Consortium (10.1016/j.ijar.2022.09.004_br0020) 2013; 381 Nagase (10.1016/j.ijar.2022.09.004_br0830) 2017; 122 Drton (10.1016/j.ijar.2022.09.004_br0360) 2004 Hyttinen (10.1016/j.ijar.2022.09.004_br0850) 2014 Nogueira (10.1016/j.ijar.2022.09.004_br0100) 2022 Guo (10.1016/j.ijar.2022.09.004_br0110) 2021; 53 Berry (10.1016/j.ijar.2022.09.004_br0820) 1984 Shpitser (10.1016/j.ijar.2022.09.004_br0880) 2008; 9 Kocaoglu (10.1016/j.ijar.2022.09.004_br0910) 2019 Yu (10.1016/j.ijar.2022.09.004_br1260) 2010 Shen (10.1016/j.ijar.2022.09.004_br1190) 2020; 10 Colombo (10.1016/j.ijar.2022.09.004_br0390) Hoyer (10.1016/j.ijar.2022.09.004_br0460) 2006 Klein (10.1016/j.ijar.2022.09.004_br0980) 2015; 161 Anker (10.1016/j.ijar.2022.09.004_br1210) 2019; 43 Nandy (10.1016/j.ijar.2022.09.004_br0420) 2018 Glymour (10.1016/j.ijar.2022.09.004_br0050) 2019; 10 Yang (10.1016/j.ijar.2022.09.004_br0290) 2018 Solus (10.1016/j.ijar.2022.09.004_br0940) Andrews (10.1016/j.ijar.2022.09.004_br0610) 2019 Moraffah (10.1016/j.ijar.2022.09.004_br0120) 2021 Tsamardinos (10.1016/j.ijar.2022.09.004_br0770) 2006; 65 Geiger (10.1016/j.ijar.2022.09.004_br0700) 1994 Hu (10.1016/j.ijar.2022.09.004_br0930) 2014 Gao (10.1016/j.ijar.2022.09.004_br1270) Janzing (10.1016/j.ijar.2022.09.004_br0740) 2010; 56 Eberhardt (10.1016/j.ijar.2022.09.004_br0920) Rubenstein (10.1016/j.ijar.2022.09.004_br0210) 2018 Han (10.1016/j.ijar.2022.09.004_br0990) 2018; 46 Colombo (10.1016/j.ijar.2022.09.004_br1140) 2012; 40 Kalainathan (10.1016/j.ijar.2022.09.004_br1080) Kocaoglu (10.1016/j.ijar.2022.09.004_br0320) 2017 Hill (10.1016/j.ijar.2022.09.004_br0030) 2011; 20 Marbach (10.1016/j.ijar.2022.09.004_br1030) 2009; 16 Peters (10.1016/j.ijar.2022.09.004_br1050) 2015; 27 Pearl (10.1016/j.ijar.2022.09.004_br0040) 2018 Comon (10.1016/j.ijar.2022.09.004_br0760) 1994; 36 Castillo (10.1016/j.ijar.2022.09.004_br0590) 2012 Tsamardinos (10.1016/j.ijar.2022.09.004_br1110) 2003 Peters (10.1016/j.ijar.2022.09.004_br0370) 2017 Van den Bulcke (10.1016/j.ijar.2022.09.004_br1010) 2006; 7 Brouillard (10.1016/j.ijar.2022.09.004_br0580) Scutari (10.1016/j.ijar.2022.09.004_br1120) 2017; 77 Stegle (10.1016/j.ijar.2022.09.004_br0750) 2010; 23 Koller (10.1016/j.ijar.2022.09.004_br0600) 2009 Vowels (10.1016/j.ijar.2022.09.004_br0140) 2021 Shahbazinia (10.1016/j.ijar.2022.09.004_br0220) Strobl (10.1016/j.ijar.2022.09.004_br1220) 2018; 6 Nogueira (10.1016/j.ijar.2022.09.004_br0150) 2021; 8 Rantanen (10.1016/j.ijar.2022.09.004_br0570) 2020 Spirtes (10.1016/j.ijar.2022.09.004_br1150) 2001 Meek (10.1016/j.ijar.2022.09.004_br0720) 1997 Miley (10.1016/j.ijar.2022.09.004_br1200) 2021 Addo (10.1016/j.ijar.2022.09.004_br1180) 2021; 7 |
| References_xml | – year: 1984 ident: br0820 article-title: Nonrecursive Causal Models, vol. 37 – year: 2019 ident: br1080 article-title: Causal discovery toolbox: uncover causal relationships in Python – year: 2018 ident: br0040 article-title: Theoretical impediments to machine learning with seven sparks from the causal revolution publication-title: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining – year: 2008 ident: br0490 article-title: Discovering cyclic causal models by independent components analysis publication-title: UAI – volume: 36 start-page: 287 year: 1994 end-page: 314 ident: br0760 article-title: Independent component analysis, a new concept? publication-title: Signal Process. – start-page: 9551 year: 2020 end-page: 9561 ident: br0550 article-title: Causal discovery from soft interventions with unknown targets: characterization and learning publication-title: Advances in Neural Information Processing Systems, vol. 33 – volume: 65 start-page: 31 year: 2006 end-page: 78 ident: br0770 article-title: The max-min hill-climbing Bayesian network structure learning algorithm publication-title: Mach. Learn. – year: 2018 ident: br0200 article-title: From random differential equations to structural causal models: the stochastic case – year: 2006 ident: br0460 article-title: Estimation of linear, non-Gaussian causal models in the presence of confounding latent variables publication-title: Probabilistic Graphical Models – year: 2018 ident: br0420 article-title: High-dimensional consistency in score-based and hybrid structure learning publication-title: Ann. Stat. – year: 2013 ident: br0390 article-title: Order-independent constraint-based causal structure learning – volume: 12 year: 2020 ident: br0640 article-title: On nonparametric conditional independence tests for continuous variables publication-title: Wiley Interdiscip. Rev.: Comput. Stat. – start-page: 340 year: 2014 end-page: 349 ident: br0850 article-title: Constraint-based causal discovery: conflict resolution with answer set programming publication-title: Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, UAI'14 – volume: 381 start-page: 1371 year: 2013 end-page: 1379 ident: br0020 article-title: Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis publication-title: Lancet – start-page: 255 year: 1990 end-page: 270 ident: br0270 article-title: Equivalence and synthesis of causal models publication-title: Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence, UAI '90 – volume: 14 start-page: 465 year: 1978 end-page: 471 ident: br0730 article-title: Modeling by shortest data description publication-title: Automatica – year: 2018 ident: br0470 article-title: DAGs with no tears: continuous optimization for structure learning – volume: 42 start-page: 2526 year: 2014 end-page: 2556 ident: br1090 article-title: CAM: causal additive models, high-dimensional order search and penalized regression publication-title: Ann. Stat. – volume: 115 start-page: 235 year: 2019 end-page: 253 ident: br0800 article-title: Who learns better Bayesian network structures: accuracy and speed of structure learning algorithms publication-title: Int. J. Approx. Reason. – volume: 7 start-page: 1 year: 2006 end-page: 12 ident: br1010 article-title: SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms publication-title: BMC Bioinform. – year: 2009 ident: br0600 article-title: Probabilistic Graphical Models: Principles and Techniques – start-page: 1 year: 2021 end-page: 45 ident: br0120 article-title: Causal inference for time series analysis: problems, methods and evaluation publication-title: Knowl. Inf. Syst. – year: 2012 ident: br0590 article-title: Expert Systems and Probabilistic Network Models – volume: 32 start-page: 2671 year: 2018 end-page: 2679 ident: br0440 article-title: Causal discovery from discrete data using hidden compact representation publication-title: Adv. Neural Inf. Process. Syst. – year: 2020 ident: br0580 article-title: Differentiable causal discovery from interventional data – volume: 41 start-page: 65 year: 2014 end-page: 98 ident: br0230 article-title: Lingam: non-Gaussian methods for estimating causal structures publication-title: Behaviormetrika – volume: 25 start-page: 505 year: 1997 end-page: 541 ident: br0300 article-title: A characterization of Markov equivalence classes for acyclic digraphs publication-title: Ann. Stat. – volume: 23 start-page: 1687 year: 2010 end-page: 1695 ident: br0750 article-title: Probabilistic latent variable models for distinguishing between cause and effect publication-title: Adv. Neural Inf. Process. Syst. – volume: 172 start-page: 1873 year: 2008 end-page: 1896 ident: br0340 article-title: On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias publication-title: Artif. Intell. – year: 2018 ident: br0210 article-title: From deterministic odes to dynamic structural causal models publication-title: UAI – year: 2019 ident: br0910 article-title: Characterization and learning of causal graphs with latent variables from soft interventions publication-title: Advances in Neural Information Processing Systems, vol. 32 – volume: 16 start-page: 1483 year: 2019 end-page: 1495 ident: br0630 article-title: A fast PC algorithm for high dimensional causal discovery with multi-core pcs publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. – volume: 3 start-page: 507 year: 2002 end-page: 554 ident: br0670 article-title: Optimal structure identification with greedy search publication-title: J. Mach. Learn. Res. – year: 2004 ident: br0360 article-title: Iterative conditional fitting for Gaussian ancestral graph models publication-title: UAI – year: 2022 ident: br0080 article-title: On pearl's hierarchy and the foundations of causal inference publication-title: Probabilistic and Causal Inference – volume: vol. 1792 start-page: 1 year: 2016 ident: br1040 article-title: Measurement Error and Causal Discovery publication-title: CEUR Workshop Proceedings – year: 2020 ident: br0060 article-title: Causal Inference: What If – volume: 56 start-page: 5168 year: 2010 end-page: 5194 ident: br0740 article-title: Causal inference using the algorithmic Markov condition publication-title: IEEE Trans. Inf. Theory – year: 2013 ident: br0900 article-title: Causal discovery from changes – year: 2013 ident: br0650 article-title: Causal inference in the presence of latent variables and selection bias – volume: 117 start-page: 29 year: 2020 end-page: 49 ident: br0510 article-title: Discovering causal graphs with cycles and latent confounders: an exact branch-and-bound approach publication-title: Int. J. Approx. Reason. – start-page: 15 year: 2015 end-page: 31 ident: br0790 article-title: Constraint-based learning Bayesian networks using Bayes factor publication-title: Workshop on Advanced Methodologies for Bayesian Networks – volume: 167 start-page: 1853 year: 2016 end-page: 1866 ident: br1000 article-title: Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens publication-title: Cell – start-page: 5541 year: 2018 end-page: 5550 ident: br0290 article-title: Characterizing and learning equivalence classes of causal DAGs under interventions publication-title: International Conference on Machine Learning – volume: 82 start-page: 669 year: 1995 end-page: 688 ident: br0260 article-title: Causal diagrams for empirical research publication-title: Biometrika – volume: 53 start-page: 1 year: 2021 end-page: 37 ident: br0110 article-title: A survey of learning causality with data: problems and methods publication-title: ACM Comput. Surv. – volume: 40 year: 2012 ident: br1140 article-title: Learning high-dimensional directed acyclic graphs with latent and selection variables publication-title: Ann. Stat. – start-page: 7021 year: 2017 end-page: 7031 ident: br0320 article-title: Experimental design for learning causal graphs with latent variables publication-title: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17 – volume: 8 start-page: 203 year: 2021 end-page: 231 ident: br0150 article-title: Causal discovery in machine learning: theories and applications publication-title: J. Dyn. Games – volume: 11 start-page: 1643 year: 2010 end-page: 1662 ident: br0810 article-title: Introduction to causal inference publication-title: J. Mach. Learn. Res. – start-page: 368 year: 2016 end-page: 379 ident: br0430 article-title: A hybrid causal search algorithm for latent variable models publication-title: Conference on Probabilistic Graphical Models – volume: 21 year: 2020 ident: br0280 article-title: Joint causal inference from multiple contexts publication-title: J. Mach. Learn. Res. – start-page: 9311 year: 2020 end-page: 9323 ident: br0450 article-title: Distinguishing cause from effect using quantiles: bivariate quantile causal discovery publication-title: International Conference on Machine Learning – volume: 47 start-page: 1 year: 2012 end-page: 26 ident: br1130 article-title: Causal inference using graphical models with the R package pcalg publication-title: J. Stat. Softw. – volume: 27 start-page: 771 year: 2015 end-page: 799 ident: br1050 article-title: Structural intervention distance for evaluating causal graphs publication-title: Neural Comput. – start-page: 111 year: 2020 end-page: 130 ident: br0380 article-title: Recent Advances in Semi-Parametric Methods for Causal Discovery – volume: 19 start-page: 716 year: 1974 end-page: 723 ident: br0680 article-title: A new look at the statistical model identification publication-title: IEEE Trans. Autom. Control – volume: 20 start-page: 217 year: 2011 end-page: 240 ident: br0030 article-title: Bayesian nonparametric modeling for causal inference publication-title: J. Comput. Graph. Stat. – volume: vol. 138 start-page: 17 year: 2020 end-page: 28 ident: br1070 article-title: Tuning causal discovery algorithms publication-title: Proceedings of the 10th International Conference on Probabilistic Graphical Models – year: 2018 ident: br0870 article-title: The Book of Why: The New Science of Cause and Effect – volume: 46 start-page: D380 year: 2018 end-page: D386 ident: br0990 article-title: TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions publication-title: Nucleic Acids Res. – year: 2014 ident: br0930 article-title: Randomized experimental design for causal graph discovery publication-title: Advances in Neural Information Processing Systems, vol. 27 – volume: 17 start-page: 1103 year: 2016 end-page: 1204 ident: br0950 article-title: Distinguishing cause from effect using observational data: methods and benchmarks publication-title: J. Mach. Learn. Res. – start-page: 1159 year: 2020 end-page: 1168 ident: br0250 article-title: Constraint-based causal discovery using partial ancestral graphs in the presence of cycles publication-title: Conference on Uncertainty in Artificial Intelligence – year: 2021 ident: br0240 article-title: Foundations of structural causal models with cycles and latent variables – volume: 6 start-page: 19 year: 2018 end-page: 30 ident: br0620 article-title: Constraint-based causal discovery with mixed data publication-title: Int. J. Data Sci. Anal. – year: 2021 ident: br1020 article-title: Dream4: Synthetic Expression Data for Gene Regulatory Network Inference from the 2009 DREAM4 Challenge – year: 2013 ident: br0480 article-title: A discovery algorithm for directed cyclic graphs – volume: 122 start-page: 109 year: 2017 end-page: 117 ident: br0830 article-title: Identifiability of nonrecursive structural equation models publication-title: Stat. Probab. Lett. – year: 2017 ident: br0370 article-title: Elements of Causal Inference: Foundations and Learning Algorithms – volume: 24 start-page: 1432 year: 2010 end-page: 1440 ident: br1060 article-title: Stability approach to regularization selection (stars) for high dimensional graphical models publication-title: Adv. Neural Inf. Process. Syst. – volume: 30 start-page: 962 year: 2002 end-page: 1030 ident: br0350 article-title: Ancestral graph Markov models publication-title: Ann. Stat. – start-page: 1 year: 2016 end-page: 28 ident: br0190 article-title: Causal discovery and inference: concepts and recent methodological advances publication-title: Applied Informatics, vol. 3 – start-page: 235 year: 1994 end-page: 243 ident: br0700 article-title: Learning Gaussian networks publication-title: Uncertainty Proceedings 1994 – start-page: 376 year: 2003 end-page: 380 ident: br1110 article-title: Algorithms for large scale Markov blanket discovery publication-title: FLAIRS Conference, vol. 2 – volume: 7 start-page: 6196 year: 2021 end-page: 6204 ident: br1180 article-title: Exploring nonlinearity on the CO2 emissions, economic production and energy use nexus: a causal discovery approach publication-title: Energy Rep. – volume: 54 start-page: 429 year: 2013 end-page: 451 ident: br0400 article-title: Scaling up the greedy equivalence search algorithm by constraining the search space of equivalence classes publication-title: Int. J. Approx. Reason. – volume: vol. R5 start-page: 214 year: 2005 end-page: 221 ident: br0890 article-title: Probabilistic soft interventions in conditional Gaussian networks publication-title: Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics – volume: 9 year: 2008 ident: br0880 article-title: Complete identification methods for the causal hierarchy publication-title: J. Mach. Learn. Res. – volume: 308 start-page: 523 year: 2005 end-page: 529 ident: br0970 article-title: Causal protein-signaling networks derived from multiparameter single-cell data publication-title: Science – volume: 77 start-page: 1 year: 2017 end-page: 20 ident: br1120 article-title: Bayesian network constraint-based structure learning algorithms: parallel and optimized implementations in the bnlearn R package publication-title: J. Stat. Softw. – volume: 16 start-page: 2147 year: 2015 end-page: 2205 ident: br1250 article-title: Constraint-based causal discovery from multiple interventions over overlapping variable sets publication-title: J. Mach. Learn. Res. – year: 2021 ident: br1270 article-title: Federated causal discovery – year: 2013 ident: br0840 article-title: Directed cyclic graphical representations of feedback models – year: 2020 ident: br0540 article-title: Permutation-based causal structure learning with unknown intervention targets – start-page: 461 year: 1978 end-page: 464 ident: br0690 article-title: Estimating the dimension of a model publication-title: Ann. Stat. – volume: 161 start-page: 1187 year: 2015 end-page: 1201 ident: br0980 article-title: Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells publication-title: Cell – year: 2020 ident: br0960 article-title: CausalWorld: a robotic manipulation benchmark for causal structure and transfer learning – start-page: e1449 year: 2022 ident: br0100 article-title: Methods and Tools for Causal Discovery and Causal Inference – year: 2015 ident: br0560 article-title: Backshift: learning causal cyclic graphs from unknown shift interventions publication-title: Advances in Neural Information Processing Systems, vol. 28 – volume: 35 start-page: 1 year: 2010 end-page: 22 ident: br1100 article-title: Learning Bayesian networks with the bnlearn R package publication-title: J. Stat. Softw. – volume: 10 start-page: 1 year: 2020 end-page: 12 ident: br1190 article-title: Challenges and opportunities with causal discovery algorithms: application to Alzheimer's pathophysiology publication-title: Sci. Rep. – year: 2021 ident: br1230 article-title: Multiple imputation and test-wise deletion for causal discovery with incomplete cohort data – year: 2017 ident: br0330 article-title: Markov properties for graphical models with cycles and latent variables – volume: 109 start-page: 612 year: 2021 end-page: 634 ident: br0160 article-title: Toward causal representation learning publication-title: Proc. IEEE – start-page: 278 year: 2001 end-page: 285 ident: br1150 article-title: An anytime algorithm for causal inference publication-title: International Workshop on Artificial Intelligence and Statistics – volume: 10 start-page: 1 year: 2019 end-page: 15 ident: br0050 article-title: Review of causal discovery methods based on graphical models publication-title: Front. Genet. – year: 1997 ident: br0720 article-title: Graphical Models: Selecting causal and statistical models – year: 2021 ident: br0940 article-title: Consistency guarantees for greedy permutation-based causal inference algorithms – volume: 13 year: 2018 ident: br0130 article-title: Causal discovery algorithms: a practical guide publication-title: Philos. Compass – year: 2016 ident: br0090 article-title: Causal Inference in Statistics: A Primer – year: 2020 ident: br0660 article-title: Generalized transportability: synthesis of experiments from heterogeneous domains publication-title: Proceedings of the 34th AAAI Conference on Artificial Intelligence – volume: 13 start-page: 2409 year: 2012 end-page: 2464 ident: br0530 article-title: Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs publication-title: J. Mach. Learn. Res. – year: 2017 ident: br0860 article-title: Ancestral causal inference – year: 2018 ident: br1160 article-title: TETRAD—a toolbox for causal discovery publication-title: 8th International Workshop on Climate Informatics – year: 2018 ident: br0170 article-title: Bayesian networks publication-title: Encyclopedia of Social Network Analysis and Mining – start-page: 142 year: 2017 end-page: 157 ident: br1170 article-title: Discovery of causal models that contain latent variables through Bayesian scoring of independence constraints publication-title: Joint European Conference on Machine Learning and Knowledge Discovery in Databases – year: 2021 ident: br0220 article-title: Paralingam: parallel causal structure learning for linear non-Gaussian acyclic models – start-page: 365 year: 2020 end-page: 376 ident: br0570 article-title: Learning optimal cyclic causal graphs from interventional data publication-title: International Conference on Probabilistic Graphical Models – year: 2021 ident: br0140 article-title: D'ya like DAGs? A survey on structure learning and causal discovery publication-title: ACM Comput. Surv. – start-page: 438 year: 2016 end-page: 448 ident: br0710 article-title: An empirical-Bayes score for discrete Bayesian networks publication-title: Conference on Probabilistic Graphical Models – year: 2013 ident: br0310 article-title: Causal inference and causal explanation with background knowledge – volume: 3 start-page: 121 year: 2017 end-page: 129 ident: br0410 article-title: A million variables and more: the fast greedy equivalence search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images publication-title: Int. J. Data Sci. Anal. – volume: 86 start-page: 4 year: 2004 end-page: 29 ident: br0010 article-title: Nonparametric estimation of average treatment effects under exogeneity: a review publication-title: Rev. Econ. Stat. – year: 2021 ident: br0180 article-title: Causal inference for process understanding in Earth sciences – year: 2018 ident: br0520 article-title: Constraint-based causal discovery for non-linear structural causal models with cycles and latent confounders – year: 2012 ident: br0920 article-title: On the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables – volume: 6 start-page: 47 year: 2018 end-page: 62 ident: br1220 article-title: Fast causal inference with non-random missingness by test-wise deletion publication-title: Int. J. Data Sci. Anal. – year: 2017 ident: br0500 article-title: A core-guided approach to learning optimal causal graphs publication-title: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), International Joint Conferences on Artificial Intelligence – start-page: 1163 year: 2010 end-page: 1168 ident: br1260 article-title: Causal discovery from streaming features publication-title: 2010 IEEE International Conference on Data Mining – volume: 21 start-page: 1 year: 2020 end-page: 53 ident: br1240 article-title: Causal discovery from heterogeneous/nonstationary data publication-title: J. Mach. Learn. Res. – start-page: 4 year: 2019 end-page: 21 ident: br0610 article-title: Learning high-dimensional directed acyclic graphs with mixed data-types publication-title: The 2019 ACM SIGKDD Workshop on Causal Discovery – volume: 16 start-page: 229 year: 2009 end-page: 239 ident: br1030 article-title: Generating realistic in silico gene networks for performance assessment of reverse engineering methods publication-title: J. Comput. Biol. – year: 2000 ident: br0070 article-title: Causation, Prediction, and Search – year: 2012 ident: br0780 article-title: Local structure discovery in Bayesian networks publication-title: UAI – volume: 43 start-page: 91 year: 2019 end-page: 97 ident: br1210 article-title: Causal network modeling of the determinants of drinking behavior in comorbid alcohol use and anxiety disorder publication-title: Alcohol. Clin. Exp. Res. – start-page: 1 year: 2021 end-page: 9 ident: br1200 article-title: Causal pathways to social and occupational functioning in the first episode of schizophrenia: uncovering unmet treatment needs publication-title: Psychol. Med. – ident: 10.1016/j.ijar.2022.09.004_br0840 – year: 2018 ident: 10.1016/j.ijar.2022.09.004_br0870 – ident: 10.1016/j.ijar.2022.09.004_br0470 – year: 2015 ident: 10.1016/j.ijar.2022.09.004_br0560 article-title: Backshift: learning causal cyclic graphs from unknown shift interventions – volume: 41 start-page: 65 year: 2014 ident: 10.1016/j.ijar.2022.09.004_br0230 article-title: Lingam: non-Gaussian methods for estimating causal structures publication-title: Behaviormetrika doi: 10.2333/bhmk.41.65 – year: 2006 ident: 10.1016/j.ijar.2022.09.004_br0460 article-title: Estimation of linear, non-Gaussian causal models in the presence of confounding latent variables – volume: 46 start-page: D380 year: 2018 ident: 10.1016/j.ijar.2022.09.004_br0990 article-title: TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkx1013 – start-page: e1449 year: 2022 ident: 10.1016/j.ijar.2022.09.004_br0100 – year: 2012 ident: 10.1016/j.ijar.2022.09.004_br0590 – start-page: 4 year: 2019 ident: 10.1016/j.ijar.2022.09.004_br0610 article-title: Learning high-dimensional directed acyclic graphs with mixed data-types – start-page: 461 year: 1978 ident: 10.1016/j.ijar.2022.09.004_br0690 article-title: Estimating the dimension of a model publication-title: Ann. Stat. – start-page: 5541 year: 2018 ident: 10.1016/j.ijar.2022.09.004_br0290 article-title: Characterizing and learning equivalence classes of causal DAGs under interventions – year: 2020 ident: 10.1016/j.ijar.2022.09.004_br0060 – start-page: 9311 year: 2020 ident: 10.1016/j.ijar.2022.09.004_br0450 article-title: Distinguishing cause from effect using quantiles: bivariate quantile causal discovery – year: 2008 ident: 10.1016/j.ijar.2022.09.004_br0490 article-title: Discovering cyclic causal models by independent components analysis – volume: 21 year: 2020 ident: 10.1016/j.ijar.2022.09.004_br0280 article-title: Joint causal inference from multiple contexts publication-title: J. Mach. Learn. Res. – year: 1997 ident: 10.1016/j.ijar.2022.09.004_br0720 – volume: 54 start-page: 429 year: 2013 ident: 10.1016/j.ijar.2022.09.004_br0400 article-title: Scaling up the greedy equivalence search algorithm by constraining the search space of equivalence classes publication-title: Int. J. Approx. Reason. doi: 10.1016/j.ijar.2012.09.004 – start-page: 1 year: 2016 ident: 10.1016/j.ijar.2022.09.004_br0190 article-title: Causal discovery and inference: concepts and recent methodological advances – start-page: 365 year: 2020 ident: 10.1016/j.ijar.2022.09.004_br0570 article-title: Learning optimal cyclic causal graphs from interventional data – start-page: 255 year: 1990 ident: 10.1016/j.ijar.2022.09.004_br0270 article-title: Equivalence and synthesis of causal models – year: 2004 ident: 10.1016/j.ijar.2022.09.004_br0360 article-title: Iterative conditional fitting for Gaussian ancestral graph models – ident: 10.1016/j.ijar.2022.09.004_br0960 – volume: 53 start-page: 1 year: 2021 ident: 10.1016/j.ijar.2022.09.004_br0110 article-title: A survey of learning causality with data: problems and methods publication-title: ACM Comput. Surv. – volume: 7 start-page: 6196 year: 2021 ident: 10.1016/j.ijar.2022.09.004_br1180 article-title: Exploring nonlinearity on the CO2 emissions, economic production and energy use nexus: a causal discovery approach publication-title: Energy Rep. doi: 10.1016/j.egyr.2021.09.026 – year: 2018 ident: 10.1016/j.ijar.2022.09.004_br0210 article-title: From deterministic odes to dynamic structural causal models – volume: 19 start-page: 716 year: 1974 ident: 10.1016/j.ijar.2022.09.004_br0680 article-title: A new look at the statistical model identification publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.1974.1100705 – start-page: 9551 year: 2020 ident: 10.1016/j.ijar.2022.09.004_br0550 article-title: Causal discovery from soft interventions with unknown targets: characterization and learning – volume: 17 start-page: 1103 year: 2016 ident: 10.1016/j.ijar.2022.09.004_br0950 article-title: Distinguishing cause from effect using observational data: methods and benchmarks publication-title: J. Mach. Learn. Res. – volume: 109 start-page: 612 year: 2021 ident: 10.1016/j.ijar.2022.09.004_br0160 article-title: Toward causal representation learning publication-title: Proc. IEEE doi: 10.1109/JPROC.2021.3058954 – ident: 10.1016/j.ijar.2022.09.004_br0180 – start-page: 1 year: 2021 ident: 10.1016/j.ijar.2022.09.004_br1200 article-title: Causal pathways to social and occupational functioning in the first episode of schizophrenia: uncovering unmet treatment needs publication-title: Psychol. Med. – start-page: 340 year: 2014 ident: 10.1016/j.ijar.2022.09.004_br0850 article-title: Constraint-based causal discovery: conflict resolution with answer set programming – volume: 13 start-page: 2409 year: 2012 ident: 10.1016/j.ijar.2022.09.004_br0530 article-title: Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs publication-title: J. Mach. Learn. Res. – volume: 8 start-page: 203 year: 2021 ident: 10.1016/j.ijar.2022.09.004_br0150 article-title: Causal discovery in machine learning: theories and applications publication-title: J. Dyn. Games doi: 10.3934/jdg.2021008 – start-page: 278 year: 2001 ident: 10.1016/j.ijar.2022.09.004_br1150 article-title: An anytime algorithm for causal inference – ident: 10.1016/j.ijar.2022.09.004_br0900 – year: 2018 ident: 10.1016/j.ijar.2022.09.004_br0170 article-title: Bayesian networks – ident: 10.1016/j.ijar.2022.09.004_br0330 – volume: 23 start-page: 1687 year: 2010 ident: 10.1016/j.ijar.2022.09.004_br0750 article-title: Probabilistic latent variable models for distinguishing between cause and effect publication-title: Adv. Neural Inf. Process. Syst. – start-page: 1159 year: 2020 ident: 10.1016/j.ijar.2022.09.004_br0250 article-title: Constraint-based causal discovery using partial ancestral graphs in the presence of cycles – ident: 10.1016/j.ijar.2022.09.004_br0220 – volume: 11 start-page: 1643 year: 2010 ident: 10.1016/j.ijar.2022.09.004_br0810 article-title: Introduction to causal inference publication-title: J. Mach. Learn. Res. – ident: 10.1016/j.ijar.2022.09.004_br0310 – volume: 115 start-page: 235 year: 2019 ident: 10.1016/j.ijar.2022.09.004_br0800 article-title: Who learns better Bayesian network structures: accuracy and speed of structure learning algorithms publication-title: Int. J. Approx. Reason. doi: 10.1016/j.ijar.2019.10.003 – volume: vol. R5 start-page: 214 year: 2005 ident: 10.1016/j.ijar.2022.09.004_br0890 article-title: Probabilistic soft interventions in conditional Gaussian networks – volume: vol. 1792 start-page: 1 year: 2016 ident: 10.1016/j.ijar.2022.09.004_br1040 article-title: Measurement Error and Causal Discovery – volume: 3 start-page: 507 year: 2002 ident: 10.1016/j.ijar.2022.09.004_br0670 article-title: Optimal structure identification with greedy search publication-title: J. Mach. Learn. Res. – start-page: 376 year: 2003 ident: 10.1016/j.ijar.2022.09.004_br1110 article-title: Algorithms for large scale Markov blanket discovery – volume: 35 start-page: 1 year: 2010 ident: 10.1016/j.ijar.2022.09.004_br1100 article-title: Learning Bayesian networks with the bnlearn R package publication-title: J. Stat. Softw. doi: 10.18637/jss.v035.i03 – start-page: 438 year: 2016 ident: 10.1016/j.ijar.2022.09.004_br0710 article-title: An empirical-Bayes score for discrete Bayesian networks – year: 2012 ident: 10.1016/j.ijar.2022.09.004_br0780 article-title: Local structure discovery in Bayesian networks – volume: 16 start-page: 229 year: 2009 ident: 10.1016/j.ijar.2022.09.004_br1030 article-title: Generating realistic in silico gene networks for performance assessment of reverse engineering methods publication-title: J. Comput. Biol. doi: 10.1089/cmb.2008.09TT – start-page: 235 year: 1994 ident: 10.1016/j.ijar.2022.09.004_br0700 article-title: Learning Gaussian networks – ident: 10.1016/j.ijar.2022.09.004_br0940 – volume: 47 start-page: 1 year: 2012 ident: 10.1016/j.ijar.2022.09.004_br1130 article-title: Causal inference using graphical models with the R package pcalg publication-title: J. Stat. Softw. doi: 10.18637/jss.v047.i11 – year: 2022 ident: 10.1016/j.ijar.2022.09.004_br0080 article-title: On pearl's hierarchy and the foundations of causal inference – volume: 13 year: 2018 ident: 10.1016/j.ijar.2022.09.004_br0130 article-title: Causal discovery algorithms: a practical guide publication-title: Philos. Compass doi: 10.1111/phc3.12470 – volume: 27 start-page: 771 year: 2015 ident: 10.1016/j.ijar.2022.09.004_br1050 article-title: Structural intervention distance for evaluating causal graphs publication-title: Neural Comput. doi: 10.1162/NECO_a_00708 – volume: 86 start-page: 4 year: 2004 ident: 10.1016/j.ijar.2022.09.004_br0010 article-title: Nonparametric estimation of average treatment effects under exogeneity: a review publication-title: Rev. Econ. Stat. doi: 10.1162/003465304323023651 – volume: 14 start-page: 465 year: 1978 ident: 10.1016/j.ijar.2022.09.004_br0730 article-title: Modeling by shortest data description publication-title: Automatica doi: 10.1016/0005-1098(78)90005-5 – volume: 36 start-page: 287 year: 1994 ident: 10.1016/j.ijar.2022.09.004_br0760 article-title: Independent component analysis, a new concept? publication-title: Signal Process. doi: 10.1016/0165-1684(94)90029-9 – ident: 10.1016/j.ijar.2022.09.004_br1230 – year: 2017 ident: 10.1016/j.ijar.2022.09.004_br0500 article-title: A core-guided approach to learning optimal causal graphs – year: 2009 ident: 10.1016/j.ijar.2022.09.004_br0600 – start-page: 1 year: 2021 ident: 10.1016/j.ijar.2022.09.004_br0120 article-title: Causal inference for time series analysis: problems, methods and evaluation publication-title: Knowl. Inf. Syst. – volume: 167 start-page: 1853 year: 2016 ident: 10.1016/j.ijar.2022.09.004_br1000 article-title: Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens publication-title: Cell doi: 10.1016/j.cell.2016.11.038 – year: 2000 ident: 10.1016/j.ijar.2022.09.004_br0070 – ident: 10.1016/j.ijar.2022.09.004_br0390 – volume: 117 start-page: 29 year: 2020 ident: 10.1016/j.ijar.2022.09.004_br0510 article-title: Discovering causal graphs with cycles and latent confounders: an exact branch-and-bound approach publication-title: Int. J. Approx. Reason. doi: 10.1016/j.ijar.2019.10.009 – volume: 381 start-page: 1371 year: 2013 ident: 10.1016/j.ijar.2022.09.004_br0020 article-title: Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis publication-title: Lancet doi: 10.1016/S0140-6736(12)62129-1 – ident: 10.1016/j.ijar.2022.09.004_br0200 – volume: 30 start-page: 962 year: 2002 ident: 10.1016/j.ijar.2022.09.004_br0350 article-title: Ancestral graph Markov models publication-title: Ann. Stat. doi: 10.1214/aos/1031689015 – year: 2018 ident: 10.1016/j.ijar.2022.09.004_br0420 article-title: High-dimensional consistency in score-based and hybrid structure learning publication-title: Ann. Stat. doi: 10.1214/17-AOS1654 – volume: 122 start-page: 109 year: 2017 ident: 10.1016/j.ijar.2022.09.004_br0830 article-title: Identifiability of nonrecursive structural equation models publication-title: Stat. Probab. Lett. doi: 10.1016/j.spl.2016.11.010 – volume: 20 start-page: 217 year: 2011 ident: 10.1016/j.ijar.2022.09.004_br0030 article-title: Bayesian nonparametric modeling for causal inference publication-title: J. Comput. Graph. Stat. doi: 10.1198/jcgs.2010.08162 – year: 2017 ident: 10.1016/j.ijar.2022.09.004_br0370 – ident: 10.1016/j.ijar.2022.09.004_br0480 – ident: 10.1016/j.ijar.2022.09.004_br0920 – ident: 10.1016/j.ijar.2022.09.004_br0240 – start-page: 1163 year: 2010 ident: 10.1016/j.ijar.2022.09.004_br1260 article-title: Causal discovery from streaming features – volume: 12 year: 2020 ident: 10.1016/j.ijar.2022.09.004_br0640 article-title: On nonparametric conditional independence tests for continuous variables publication-title: Wiley Interdiscip. Rev.: Comput. Stat. – volume: 7 start-page: 1 year: 2006 ident: 10.1016/j.ijar.2022.09.004_br1010 article-title: SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms publication-title: BMC Bioinform. doi: 10.1186/1471-2105-7-43 – volume: 161 start-page: 1187 year: 2015 ident: 10.1016/j.ijar.2022.09.004_br0980 article-title: Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells publication-title: Cell doi: 10.1016/j.cell.2015.04.044 – start-page: 368 year: 2016 ident: 10.1016/j.ijar.2022.09.004_br0430 article-title: A hybrid causal search algorithm for latent variable models – year: 1984 ident: 10.1016/j.ijar.2022.09.004_br0820 – volume: 6 start-page: 47 year: 2018 ident: 10.1016/j.ijar.2022.09.004_br1220 article-title: Fast causal inference with non-random missingness by test-wise deletion publication-title: Int. J. Data Sci. Anal. doi: 10.1007/s41060-017-0094-6 – ident: 10.1016/j.ijar.2022.09.004_br0540 – year: 2018 ident: 10.1016/j.ijar.2022.09.004_br0040 article-title: Theoretical impediments to machine learning with seven sparks from the causal revolution – volume: vol. 138 start-page: 17 year: 2020 ident: 10.1016/j.ijar.2022.09.004_br1070 article-title: Tuning causal discovery algorithms – ident: 10.1016/j.ijar.2022.09.004_br1270 – year: 2016 ident: 10.1016/j.ijar.2022.09.004_br0090 – volume: 16 start-page: 2147 year: 2015 ident: 10.1016/j.ijar.2022.09.004_br1250 article-title: Constraint-based causal discovery from multiple interventions over overlapping variable sets publication-title: J. Mach. Learn. Res. – ident: 10.1016/j.ijar.2022.09.004_br1080 – volume: 43 start-page: 91 year: 2019 ident: 10.1016/j.ijar.2022.09.004_br1210 article-title: Causal network modeling of the determinants of drinking behavior in comorbid alcohol use and anxiety disorder publication-title: Alcohol. Clin. Exp. Res. doi: 10.1111/acer.13914 – start-page: 111 year: 2020 ident: 10.1016/j.ijar.2022.09.004_br0380 – volume: 24 start-page: 1432 issue: 2 year: 2010 ident: 10.1016/j.ijar.2022.09.004_br1060 article-title: Stability approach to regularization selection (stars) for high dimensional graphical models publication-title: Adv. Neural Inf. Process. Syst. – volume: 10 start-page: 1 year: 2019 ident: 10.1016/j.ijar.2022.09.004_br0050 article-title: Review of causal discovery methods based on graphical models publication-title: Front. Genet. doi: 10.3389/fgene.2019.00524 – volume: 16 start-page: 1483 year: 2019 ident: 10.1016/j.ijar.2022.09.004_br0630 article-title: A fast PC algorithm for high dimensional causal discovery with multi-core pcs publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. doi: 10.1109/TCBB.2016.2591526 – year: 2018 ident: 10.1016/j.ijar.2022.09.004_br1160 article-title: TETRAD—a toolbox for causal discovery – ident: 10.1016/j.ijar.2022.09.004_br0520 – start-page: 7021 year: 2017 ident: 10.1016/j.ijar.2022.09.004_br0320 article-title: Experimental design for learning causal graphs with latent variables – volume: 308 start-page: 523 year: 2005 ident: 10.1016/j.ijar.2022.09.004_br0970 article-title: Causal protein-signaling networks derived from multiparameter single-cell data publication-title: Science doi: 10.1126/science.1105809 – volume: 25 start-page: 505 year: 1997 ident: 10.1016/j.ijar.2022.09.004_br0300 article-title: A characterization of Markov equivalence classes for acyclic digraphs publication-title: Ann. Stat. doi: 10.1214/aos/1031833662 – volume: 65 start-page: 31 year: 2006 ident: 10.1016/j.ijar.2022.09.004_br0770 article-title: The max-min hill-climbing Bayesian network structure learning algorithm publication-title: Mach. Learn. doi: 10.1007/s10994-006-6889-7 – volume: 3 start-page: 121 year: 2017 ident: 10.1016/j.ijar.2022.09.004_br0410 article-title: A million variables and more: the fast greedy equivalence search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images publication-title: Int. J. Data Sci. Anal. doi: 10.1007/s41060-016-0032-z – year: 2014 ident: 10.1016/j.ijar.2022.09.004_br0930 article-title: Randomized experimental design for causal graph discovery – volume: 10 start-page: 1 year: 2020 ident: 10.1016/j.ijar.2022.09.004_br1190 article-title: Challenges and opportunities with causal discovery algorithms: application to Alzheimer's pathophysiology publication-title: Sci. Rep. – volume: 21 start-page: 1 year: 2020 ident: 10.1016/j.ijar.2022.09.004_br1240 article-title: Causal discovery from heterogeneous/nonstationary data publication-title: J. Mach. Learn. Res. – year: 2019 ident: 10.1016/j.ijar.2022.09.004_br0910 article-title: Characterization and learning of causal graphs with latent variables from soft interventions – ident: 10.1016/j.ijar.2022.09.004_br0580 – volume: 40 year: 2012 ident: 10.1016/j.ijar.2022.09.004_br1140 article-title: Learning high-dimensional directed acyclic graphs with latent and selection variables publication-title: Ann. Stat. doi: 10.1214/11-AOS940 – year: 2020 ident: 10.1016/j.ijar.2022.09.004_br0660 article-title: Generalized transportability: synthesis of experiments from heterogeneous domains – volume: 172 start-page: 1873 year: 2008 ident: 10.1016/j.ijar.2022.09.004_br0340 article-title: On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias publication-title: Artif. Intell. doi: 10.1016/j.artint.2008.08.001 – ident: 10.1016/j.ijar.2022.09.004_br0860 – volume: 77 start-page: 1 year: 2017 ident: 10.1016/j.ijar.2022.09.004_br1120 article-title: Bayesian network constraint-based structure learning algorithms: parallel and optimized implementations in the bnlearn R package publication-title: J. Stat. Softw. doi: 10.18637/jss.v077.i02 – volume: 9 year: 2008 ident: 10.1016/j.ijar.2022.09.004_br0880 article-title: Complete identification methods for the causal hierarchy publication-title: J. Mach. Learn. Res. – volume: 6 start-page: 19 year: 2018 ident: 10.1016/j.ijar.2022.09.004_br0620 article-title: Constraint-based causal discovery with mixed data publication-title: Int. J. Data Sci. Anal. doi: 10.1007/s41060-018-0097-y – volume: 56 start-page: 5168 year: 2010 ident: 10.1016/j.ijar.2022.09.004_br0740 article-title: Causal inference using the algorithmic Markov condition publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.2010.2060095 – year: 2021 ident: 10.1016/j.ijar.2022.09.004_br1020 – year: 2021 ident: 10.1016/j.ijar.2022.09.004_br0140 article-title: D'ya like DAGs? A survey on structure learning and causal discovery publication-title: ACM Comput. Surv. – volume: 42 start-page: 2526 year: 2014 ident: 10.1016/j.ijar.2022.09.004_br1090 article-title: CAM: causal additive models, high-dimensional order search and penalized regression publication-title: Ann. Stat. doi: 10.1214/14-AOS1260 – start-page: 15 year: 2015 ident: 10.1016/j.ijar.2022.09.004_br0790 article-title: Constraint-based learning Bayesian networks using Bayes factor – volume: 82 start-page: 669 year: 1995 ident: 10.1016/j.ijar.2022.09.004_br0260 article-title: Causal diagrams for empirical research publication-title: Biometrika doi: 10.1093/biomet/82.4.669 – ident: 10.1016/j.ijar.2022.09.004_br0650 – start-page: 142 year: 2017 ident: 10.1016/j.ijar.2022.09.004_br1170 article-title: Discovery of causal models that contain latent variables through Bayesian scoring of independence constraints – volume: 32 start-page: 2671 year: 2018 ident: 10.1016/j.ijar.2022.09.004_br0440 article-title: Causal discovery from discrete data using hidden compact representation publication-title: Adv. Neural Inf. Process. Syst. |
| SSID | ssj0006748 |
| Score | 2.6225572 |
| Snippet | Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 101 |
| SubjectTerms | Causal discovery Causal models Causality Structural learning |
| Title | A Survey on Causal Discovery: Theory and Practice |
| URI | https://dx.doi.org/10.1016/j.ijar.2022.09.004 |
| Volume | 151 |
| WOSCitedRecordID | wos000876728600006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-4731 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0006748 issn: 0888-613X databaseCode: AIEXJ dateStart: 20211207 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3fb9MwELbQxgMvMNgQYxvyA28ok2Pnh723auoECE08DNS3yHYdKaVkU9NNhb9-Z5-TdYNN7IGXqIpsp_Vdzt9d774j5H3NajB6WZ5klhdJphhLjLQiEcxNAZ_YwoVswu9fytNTOZmorzFPtwvtBMq2lauVuvivooZ7IGxfOvsIcQ-Lwg34DEKHK4gdrv8k-BEYg8UVvOog12N92QW71lmfqvkr5lj4_9WxSgBLpNYR6u0Q4RqxRGAfXzWAcH2nFd2FOO4Qd9ZtjNDOQ17tELv9_aNZND-xBns8b-ohouOrV8KME23i-Bh84HwtkaO3URK8z9DS98agRgpZNIlpHI-na4rxjT8MN8YQZofNTHuWVs4D-yx2Jr7Nkn3n9BpyCvt0tVnl16j8GhVTVSCL3eRlrsDmbY4-jSefh5PaN1pBLwN_RCyqwvy_u9_k78BlDYycbZHn0YugI5T-S_LEta_Ii75DB40Ge5ukI4rKQM9bispAB2U4oqgKFFSB9qqwQ76djM-OPyaxSUZiBWPLRBvhUoBZsihN6VjBual1Jhz4IQqwvRJG6kyndVnYqckEd4VNrWK51vlU1kyI12SjPW_dG0KVAOcgd7qQymRFXivP9lcDgDHTTCrLd0na70BlI4O8b2Qyr-7f-13yYZhzgfwpD47O-42tIgJEZFeBnjww7-2jnrJHnt3o8j7ZWC4u3QF5aq-WTbd4F5XkGsQGecg |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Survey+on+Causal+Discovery%3A+Theory+and+Practice&rft.jtitle=International+journal+of+approximate+reasoning&rft.au=Zanga%2C+Alessio&rft.au=Ozkirimli%2C+Elif&rft.au=Stella%2C+Fabio&rft.date=2022-12-01&rft.issn=0888-613X&rft.volume=151&rft.spage=101&rft.epage=129&rft_id=info:doi/10.1016%2Fj.ijar.2022.09.004&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_ijar_2022_09_004 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0888-613X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0888-613X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0888-613X&client=summon |