Efficient nonparametric causal inference with missing exposure information

TL;DR

This paper develops new nonparametric methods for estimating causal effects in observational studies with missing treatment data, accounting for complex missingness mechanisms and enabling efficient, robust inference even with machine learning techniques.

Contribution

It introduces a new identifying expression and efficient influence function for causal effects with missing exposure, along with estimators that are less sensitive to high dimensionality and suitable for flexible models.

Findings

Derived a new identifying expression for average treatment effects with missing data.

Constructed nonparametric estimators with faster convergence rates.

Demonstrated root-n consistency and asymptotic normality under weak conditions.

Abstract

Missing exposure information is a very common feature of many observational studies. Here we study identifiability and efficient estimation of causal effects on vector outcomes, in such cases where treatment is unconfounded but partially missing. We consider a missing at random setting where missingness in treatment can depend not only on complex covariates, but also on post-treatment outcomes. We give a new identifying expression for average treatment effects in this setting, along with the efficient influence function for this parameter in a nonparametric model, which yields a nonparametric efficiency bound. We use this latter result to construct nonparametric estimators that are less sensitive to the curse of dimensionality than usual, e.g., by having faster rates of convergence than the complex nuisance estimators they rely on. Further we show that these estimators can be root-n consistent and asymptotically normal under weak nonparametric conditions, even when constructed using flexible machine learning. Finally we apply these results to the problem of causal inference with a partially missing instrumental variable.