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

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 propen...

Full description

Saved in:
Bibliographic Details
Published in:Communications in statistics. Simulation and computation 2017-01, Vol.46 (5), p.3458-3478
Main Authors: Pingel, Ronnie, Waernbaum, Ingeborg
Format: Article
Language:English
Subjects:
62F10
62G05
62G35
central nervous system
Doubly robust
Estimators
Inverse probability
Matching
Mathematical models
Observational studies
Observational study
Robustness (mathematics)
Statistics
Statistik
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 on 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 covariate correlations 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 toward outcome and treatment, and whether a constant or non-constant causal effect is present.
ISSN:0361-0918
1532-4141
1532-4141
DOI:10.1080/03610918.2015.1094091