Correlation and efficiency of propensity score-based estimators for average causal effects

Ronnie Pingel () and Ingeborg Waernbaum ()
Additional contact information
Ronnie Pingel: Department of Statistics, Uppsala University, Postal: SE-751 20 Uppsala, Sweden
Ingeborg Waernbaum: IFAU - Institute for Evaluation of Labour Market and Education Policy, Postal: P O Box 513, SE-751 20 Uppsala, Sweden

No 2015:3, Working Paper Series from IFAU - Institute for Evaluation of Labour Market and Education Policy

Abstract: Propensity score based-estimators are commonly used to estimate causal effects in evaluation research. To reduce bias in observational studies researchers might be tempted to include many, perhaps correlated, covariates when estimating the propensity score model. Taking into account that the propensity score is estimated, this study investigates how the efficiency of matching, inverse probability weighting and doubly robust estimators change under the case of correlated covariates. Propositions regarding the large sample variances under certain assumptions of the data generating process are given. The propositions are supplemented by several numerical large sample and finite sample results from a wide range of models. The results show that the correlation may increase or decrease the variances of the estimators. There are several factors that influence how correlation affects the variance of the estimators, including the choice of estimator, the strength of the confounding towards outcome and treatment, and whether a constant or non-constant causal effect is present.

Keywords: Double robust; inverse probability weighting; matching; observational study (search for similar items in EconPapers)
JEL-codes: C13 C40 C52 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2015-02-12
New Economics Papers: this item is included in nep-ecm
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Citations:

Forthcoming as Pingel, Ronnie and Ingeborg Waernbaum, 'Correlation and efficiency of propensity score-based estimators for average causal effects' in Communications in Statistics - Simulation and Computation, 2015.

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