Nonparametric identification and efficient estimation of causal effects with instrumental variables
arxiv(2024)
摘要
Instrumental variables are widely used in econometrics and epidemiology for
identifying and estimating causal effects when an exposure of interest is
confounded by unmeasured factors. Despite this popularity, the assumptions
invoked to justify the use of instruments differ substantially across the
literature. Similarly, statistical approaches for estimating the resulting
causal quantities vary considerably, and often rely on strong parametric
assumptions. In this work, we compile and organize structural conditions that
nonparametrically identify conditional average treatment effects, average
treatment effects among the treated, and local average treatment effects, with
a focus on identification formulae invoking the conditional Wald estimand.
Moreover, we build upon existing work and propose nonparametric efficient
estimators of functionals corresponding to marginal and conditional causal
contrasts resulting from the various identification paradigms. We illustrate
the proposed methods on an observational study examining the effects of
operative care on adverse events for cholecystitis patients, and a randomized
trial assessing the effects of market participation on political views.
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