Doubly robust matching estimators for high dimensional confounding\n adjustment

2016/12/01 by Joseph Antonelli, Antonelli, Joseph, Matthew Cefalu +5 · 1 citation
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Health Systems, Economic Evaluations, Quality of Life #Healthcare Policy and Management #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.1612.00424

openalex publication_date 2016/12/01 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28

Abstract

Valid estimation of treatment effects from observational data requires proper\ncontrol of confounding. If the number of covariates is large relative to the\nnumber of observations, then controlling for all available covariates is\ninfeasible. In cases where a sparsity condition holds, variable selection or\npenalization can reduce the dimension of the covariate space in a manner that\nallows for valid estimation of treatment effects. In this article, we propose\nmatching on both the estimated propensity score and the estimated prognostic\nscores when the number of covariates is large relative to the number of\nobservations. We derive asymptotic results for the matching estimator and show\nthat it is doubly robust, in the sense that only one of the two score models\nneed be correct to obtain a consistent estimator. We show via simulation its\neffectiveness in controlling for confounding and highlight its potential to\naddress nonlinear confounding. Finally, we apply the proposed procedure to\nanalyze the effect of gender on prescription opioid use using insurance claims\ndata.\n

Citations

• Estimating causal effects of treatments in randomized and nonrandomized studies.
• Doubly Robust Estimation in Missing Data and Causal Inference Models
• The central role of the propensity score in observational studies for causal effects

Cited by

• Horseshoe Forests for High-Dimensional Causal Survival Analysis

Related

• On propensity score matching with a diverging number of matches
• Multiply Robust Inference of Average Treatment Effects by High-dimensional Empirical Likelihood
• Doubly robust estimators for the average treatment effect under positivity violations: introducing the e-score
• Robust Estimation of Causal Effects via High-Dimensional Covariate Balancing Propensity Score
• Multiply robust matching estimators of average and quantile treatment effects