Nonparametric bounds for causal effects in imperfect randomized experiments

TL;DR

This paper develops new nonparametric bounds for causal effects in randomized experiments affected by nonignorable missing data and noncompliance, providing a way to estimate causal risk differences with minimal assumptions.

Contribution

It introduces novel bounds for causal risk differences under various missing data mechanisms and noncompliance scenarios, expanding the tools for causal inference in complex experimental settings.

Findings

Derived bounds for causal risk difference with nonignorable missingness.

Applied bounds to real data on peanut consumption and allergy development.

Demonstrated bounds' usefulness in practical experimental analysis.

Abstract

Nonignorable missingness and noncompliance can occur even in well-designed randomized experiments making the intervention effect that the experiment was designed to estimate nonidentifiable. Nonparametric causal bounds provide a way to narrow the range of possible values for a nonidentifiable causal effect with minimal assumptions. We derive novel bounds for the causal risk difference for a binary outcome and intervention in randomized experiments with nonignorable missingness caused by a variety of mechanisms and with or without noncompliance. We illustrate the use of the proposed bounds in our motivating data example of peanut consumption on the development of peanut allergies in infants.