Nonparametric identification and efficient estimation of causal effects with instrumental variables

TL;DR

This paper develops nonparametric methods for identifying and efficiently estimating various causal effects using instrumental variables, relaxing strong parametric assumptions and providing practical estimators for observational and experimental data.

Contribution

It offers a unified framework for nonparametric identification of multiple causal effects and introduces efficient estimators based on the conditional Wald estimand.

Findings

Proposed nonparametric identification formulas for causal effects.

Developed efficient estimators for these causal functionals.

Applied methods to real observational and experimental datasets.

Abstract

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.