Structural equation modeling for causal effect estimation with machine learning
Causal inference is a crucial framework in a variety of fields, such as economics, healthcare, and social science. In this context, data-driven machine learning models have become more popular for estimating the effects of treatments. One fundamental technique for causal inference is structural equa...
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| Published in: | Journal of computational and applied mathematics Vol. 475; p. 117020 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
15.03.2026
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| ISSN: | 0377-0427 |
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| Abstract | Causal inference is a crucial framework in a variety of fields, such as economics, healthcare, and social science. In this context, data-driven machine learning models have become more popular for estimating the effects of treatments. One fundamental technique for causal inference is structural equation modeling (SEM), which makes it possible to estimate the relationships between variables. However, conventional SEM methods need help with the complexities of structural causal models (SCM) in real-world data, including latent confounders, nonlinear relationships, and challenges in accurately specifying model structures. These limitations could cause the interpretation or biased causal effects. To address these challenges, we introduce a new proposed method combining the piecewise structural equation modeling (PSEM) with the backdoor criterion, named PSEMBC. The main innovation of PSEMBC is the estimation of causal effects with the use of a linear SCM model, which captures complex relationships and interactions in the data. We demonstrate the value of PSEMBC for precisely and reliably identifying the average treatment effect (ATE) in simulated and real-world datasets utilizing a comparative study with current causal inference approaches. |
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| AbstractList | Causal inference is a crucial framework in a variety of fields, such as economics, healthcare, and social science. In this context, data-driven machine learning models have become more popular for estimating the effects of treatments. One fundamental technique for causal inference is structural equation modeling (SEM), which makes it possible to estimate the relationships between variables. However, conventional SEM methods need help with the complexities of structural causal models (SCM) in real-world data, including latent confounders, nonlinear relationships, and challenges in accurately specifying model structures. These limitations could cause the interpretation or biased causal effects. To address these challenges, we introduce a new proposed method combining the piecewise structural equation modeling (PSEM) with the backdoor criterion, named PSEMBC. The main innovation of PSEMBC is the estimation of causal effects with the use of a linear SCM model, which captures complex relationships and interactions in the data. We demonstrate the value of PSEMBC for precisely and reliably identifying the average treatment effect (ATE) in simulated and real-world datasets utilizing a comparative study with current causal inference approaches. |
| ArticleNumber | 117020 |
| Author | Debbouche, Amar Ahmad, Sohail Shah, Kamal |
| Author_xml | – sequence: 1 givenname: Sohail surname: Ahmad fullname: Ahmad, Sohail email: asohail@stat.qau.edu.pk organization: School of Mathematics and Statistics, Central South University, Changsha Hunan, 410083 China – sequence: 2 givenname: Kamal surname: Shah fullname: Shah, Kamal email: kamal@uom.edu.pk organization: Department of Mathematics, University of Malakand, Chakdara Dir(L), KPK, 18000, Pakistan – sequence: 3 givenname: Amar orcidid: 0000-0003-4321-9515 surname: Debbouche fullname: Debbouche, Amar email: amar_debbouche@yahoo.fr organization: Department of Mathematics, Guelma University, Guelma 24000, Algeria |
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| Cites_doi | 10.1037/0033-2909.103.3.411 10.1016/j.jhep.2023.01.006 10.1093/aje/kwq439 10.1002/sta4.326 10.1111/ectj.12097 10.1016/j.cam.2020.113065 10.1198/jcgs.2010.08162 10.1080/01621459.2017.1398657 10.1017/rsm.2025.5 10.1111/j.1541-0420.2005.00377.x 10.1073/pnas.1510489113 10.1080/01621459.2017.1319839 10.1073/pnas.1804597116 10.1111/2041-210X.12512 10.1093/biostatistics/kxab017 10.1007/s11747-014-0403-8 10.1002/mpr.70015 10.1080/01621459.1994.10476818 10.1007/s10489-025-06738-1 10.1093/biomet/70.1.41 10.1023/A:1010933404324 10.1890/08-1034.1 10.1093/bioinformatics/btx174 |
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| Keywords | Causal inference Confounding variables Piecewise SEM Structural causal model Backdoor criterion Machine learning |
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| SubjectTerms | Backdoor criterion Causal inference Confounding variables Machine learning Piecewise SEM Structural causal model |
| Title | Structural equation modeling for causal effect estimation with machine learning |
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