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Model-free variable selection in causal inference
Umeå University, Faculty of Social Sciences, Statistics.
Manuscript (Other academic)
URN: urn:nbn:se:umu:diva-3289OAI: diva2:141830
Available from: 2008-05-30 Created: 2008-05-30 Last updated: 2010-01-13Bibliographically approved
In thesis
1. Covariate selection and propensity score specification in causal inference
Open this publication in new window or tab >>Covariate selection and propensity score specification in causal inference
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis makes contributions to the statistical research field of causal inference in observational studies. The results obtained are directly applicable in many scientific fields where effects of treatments are investigated and yet controlled experiments are difficult or impossible to implement.

In the first paper we define a partially specified directed acyclic graph (DAG) describing the independence structure of the variables under study. Using the DAG we show that given that unconfoundedness holds we can use the observed data to select minimal sets of covariates to control for. General covariate selection algorithms are proposed to target the defined minimal subsets.

The results of the first paper are generalized in Paper II to include the presence of unobserved covariates. Morevoer, the identification assumptions from the first paper are relaxed.

To implement the covariate selection without parametric assumptions we propose in the third paper the use of a model-free variable selection method from the framework of sufficient dimension reduction. By simulation the performance of the proposed selection methods are investigated. Additionally, we study finite sample properties of treatment effect estimators based on the selected covariate sets.

In paper IV we investigate misspecifications of parametric models of a scalar summary of the covariates, the propensity score. Motivated by common model specification strategies we describe misspecifications of parametric models for which unbiased estimators of the treatment effect are available. Consequences of the misspecification for the efficiency of treatment effect estimators are also studied.

Place, publisher, year, edition, pages
Umeå: Statistik, 2008
Statistical studies, ISSN 1100-8989 ; 38
Covariate selection, graphical models, matching, observational studies, treatment effects, unconfoundedness
National Category
Probability Theory and Statistics
urn:nbn:se:umu:diva-1688 (URN)978-91-7264-564-6 (ISBN)
Public defence
2008-09-12, Hörsal D, Samhällsvetarhuset, Umeå universitet, 901 87, Umeå, 13:15
Available from: 2008-05-30 Created: 2008-05-30 Last updated: 2011-03-17Bibliographically approved

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