Data-driven algorithms for dimension reduction in causal inference
In observational studies, the causal effect of a treatment may be confounded with variables that are related to both the treatment and the outcome of interest. In order to identify a causal effect, such studies often rely on the unconfoundedness assumption, i.e., that all confounding variables are o...
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| Vydáno v: | Computational statistics & data analysis Ročník 105; s. 280 - 292 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.01.2017
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| ISSN: | 0167-9473, 1872-7352, 1872-7352 |
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| Abstract | In observational studies, the causal effect of a treatment may be confounded with variables that are related to both the treatment and the outcome of interest. In order to identify a causal effect, such studies often rely on the unconfoundedness assumption, i.e., that all confounding variables are observed. The choice of covariates to control for, which is primarily based on subject matter knowledge, may result in a large covariate vector in the attempt to ensure that unconfoundedness holds. However, including redundant covariates can affect bias and efficiency of nonparametric causal effect estimators, e.g., due to the curse of dimensionality. Data-driven algorithms for the selection of sufficient covariate subsets are investigated. Under the assumption of unconfoundedness the algorithms search for minimal subsets of the covariate vector. Based, e.g., on the framework of sufficient dimension reduction or kernel smoothing, the algorithms perform a backward elimination procedure assessing the significance of each covariate. Their performance is evaluated in simulations and an application using data from the Swedish Childhood Diabetes Register is also presented. |
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| AbstractList | In observational studies, the causal effect of a treatment may be confounded with variables that are related to both the treatment and the outcome of interest. In order to identify a causal effect, such studies often rely on the unconfoundedness assumption, i.e., that all confounding variables are observed. The choice of covariates to control for, which is primarily based on subject matter knowledge, may result in a large covariate vector in the attempt to ensure that unconfoundedness holds. However, including redundant covariates can affect bias and efficiency of nonparametric causal effect estimators, e.g., due to the curse of dimensionality. Data-driven algorithms for the selection of sufficient covariate subsets are investigated. Under the assumption of unconfoundedness the algorithms search for minimal subsets of the covariate vector. Based, e.g., on the framework of sufficient dimension reduction or kernel smoothing, the algorithms perform a backward elimination procedure assessing the significance of each covariate. Their performance is evaluated in simulations and an application using data from the Swedish Childhood Diabetes Register is also presented. In observational studies, the causal effect of a treatment may be confounded with variables that are related to both the treatment and the outcome of interest. In order to identify a causal effect, such studies often rely on the unconfoundedness assumption, i.e., that all confounding variables are observed. The choice of covariates to control for, which is primarily based on subject matter knowledge, may result in a large covariate vector in the attempt to ensure that unconfoundedness holds. However, including redundant covariates can affect bias and efficiency of nonparametric causal effect estimators, e.g., due to the curse of dimensionality. In this paper, data-driven algo- rithms for the selection of sufficient covariate subsets are investigated. Under the assumption of unconfoundedness we search for minimal subsets of the covariate vector. Based on the framework of sufficient dimension reduction or kernel smoothing, the algorithms perform a backward elim- ination procedure testing the significance of each covariate. Their performance is evaluated in simulations and an application using data from the Swedish Childhood Diabetes Register is also presented. |
| Author | Persson, Emma de Luna, Xavier Waernbaum, Ingeborg Häggström, Jenny |
| Author_xml | – sequence: 1 givenname: Emma surname: Persson fullname: Persson, Emma email: emma.persson@umu.se – sequence: 2 givenname: Jenny surname: Häggström fullname: Häggström, Jenny – sequence: 3 givenname: Ingeborg surname: Waernbaum fullname: Waernbaum, Ingeborg – sequence: 4 givenname: Xavier surname: de Luna fullname: de Luna, Xavier |
| BackLink | https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-80696$$DView record from Swedish Publication Index (Umeå universitet) https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-381277$$DView record from Swedish Publication Index (Uppsala universitet) |
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| Cites_doi | 10.1198/jcgs.2010.08162 10.1080/01621459.1994.10476459 10.1214/009053604000000292 10.1093/biomet/70.1.41 10.1080/00273171.2011.570164 10.1037/1082-989X.9.4.403 10.1198/jbes.2009.0015 10.1093/biomet/87.3.706 10.1037/h0037350 10.1111/j.1541-0420.2007.00836.x 10.1177/0962280210387717 10.1111/j.1467-9868.2005.00502.x 10.1080/01621459.1996.10476968 10.1002/sim.1903 10.2337/db10-0813 10.18637/jss.v068.i01 10.1257/jel.47.1.5 10.1080/01621459.2013.869498 10.1097/00001648-199901000-00008 10.1198/016214503000000828 10.1093/aje/kwj149 10.1162/003465304323023688 10.1111/j.1468-0262.2006.00655.x 10.1093/biomet/asr041 10.1093/biomet/asm021 10.1080/10485250902928435 10.1162/rest.89.4.784 10.1162/REST_a_00153 10.1198/016214504000000548 10.3102/10769986002001001 10.1080/01621459.1991.10475035 10.7326/0003-4819-127-8_Part_2-199710151-00064 10.2202/1557-4679.1181 10.18637/jss.v033.i01 10.2307/27917241 10.1111/j.1541-0420.2011.01619.x |
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| Keywords | Matching Marginal co-ordinate hypothesis test Covariate selection Kernel smoothing Type 1 diabetes mellitus |
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