Double score matching estimators of average and quantile treatment effects
2020
Propensity score matching has a long tradition for handling confounding in
causal inference. In this article, we propose double score matching estimators
of the average treatment effects and the quantile treatment effects utilizing
two balancing scores including the propensity score and the prognostic score.
We show that the de-biasing double score matching estimators achieve the double
robustness property in that they are consistent for the true causal estimands
if either the propensity score model or the prognostic score model is correctly
specified, not necessarily both.} We characterize the asymptotic distributions
for the doubly score matching estimators when either one of the score model is
correctly specified based on the martingale representations of the matching
estimators and theory for local normal experiments. We also provide a two-stage
replication method for variance estimation and therefore doubly robust
inference. R package is available online.
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