Cited by 490Advanced Causal Inference TechniquesMeta-analysis and systematic reviewsStatistical Methods and Bayesian Inference

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The importance of covariate selection in controlling for selection bias in observational studies.

Steiner, Peter M., Cook, Thomas D., Shadish, William R., Clark, M. H.·2010·Psychological Methods

DOI 10.1037/a0018719

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Abstract

The assumption of strongly ignorable treatment assignment is required for eliminating selection bias in observational studies. To meet this assumption, researchers often rely on a strategy of selecting covariates that they think will control for selection bias. Theory indicates that the most important covariates are those highly correlated with both the real selection process and the potential outcomes. However, when planning a study, it is rarely possible to identify such covariates with certainty. In this article, we report on an extensive reanalysis of a within-study comparison that contrasts a randomized experiment and a quasi-experiment. Various covariate sets were used to adjust for initial group differences in the quasi-experiment that was characterized by self-selection into treatment. The adjusted effect sizes were then compared with the experimental ones to identify which individual covariates, and which conceptually grouped sets of covariates, were responsible for the high degree of bias reduction achieved in the adjusted quasi-experiment. Such results provide strong clues about preferred strategies for identifying the covariates most likely to reduce bias when planning a study and when the true selection process is not known.

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“After PS-adjustment, standardized mean differences d should be close to zero, and variance ratios v should be close to one (Rubin, 2001).”
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Authors: Steiner, P. M., Cook, T. D., Shadish, W. R., & Clark, M. H.
Affiliations: Northwestern University (US)
ORCID: Steiner, Peter M.
Cook, Thomas D.
Clark, M. H.
Venue: Psychological Methods
Publisher: American Psychological Association (APA)
Published: 2010
Volume: 15
Issue: 3
Pages: 250-267
ISSN: 1082-989X (ISSN-L)
1939-1463
DOI: 10.1037/a0018719
OpenAlex: W2168206775
Cited by: 490
References: 55
Citations by year: 2026: 23 · 2025: 28 · 2024: 34 · 2023: 26 · 2022: 26 · 2021: 53 · 2020: 32 · 2019: 20 · 2018: 37 · 2017: 36 · 2016: 38 · 2015: 28 · 2014: 31 · 2013: 32 · 2012: 19
FWCI: 18.95
Type: journal article
Language: en
Open access: closed
Locations: Psychological Methods (publishedVersion)
PubMed (publishedVersion)
Funders: US Department of Education (R305U070003)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords: covariate selection, selection bias, observational studies, quasi-experiments, self-selection, randomized experiments, bias mitigation
MeSH terms: Data Interpretation, Statistical, Epidemiologic Methods, Humans, Mathematics, Random Allocation, Models, Statistical, Selection Bias, Causality, Patient Selection, Behavioral Research, Propensity Score

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The importance of covariate selection in controlling for selection bias in observational studies.

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