A Bayesian Analysis of Two-Stage Randomized Experiments in the Presence of Interference, Treatment Nonadherence, and Missing Outcomes

TL;DR

This paper introduces a Bayesian causal inference method that simultaneously addresses interference, treatment nonadherence, and missing outcomes in complex two-stage randomized experiments, providing more comprehensive analysis tools.

Contribution

It develops a flexible Bayesian framework extending principal stratification to handle multiple complexities without relying on strong structural assumptions.

Findings

Method effectively handles interference, nonadherence, and missing data.

Simulation studies validate the approach's robustness and flexibility.

Re-analysis reveals new causal effects not identified previously.

Abstract

Three critical issues for causal inference that often occur in modern, complicated experiments are interference, treatment nonadherence, and missing outcomes. A great deal of research efforts has been dedicated to developing causal inferential methodologies that address these issues separately. However, methodologies that can address these issues simultaneously are lacking. We propose a Bayesian causal inference methodology to address this gap. Our methodology extends existing causal frameworks and methods, specifically, two-staged randomized experiments and the principal stratification framework. In contrast to existing methods that invoke strong structural assumptions to identify principal causal effects, our Bayesian approach uses flexible distributional models that can accommodate the complexities of interference and missing outcomes, and that ensure that principal causal effects are weakly identifiable. We illustrate our methodology via simulation studies and a re-analysis of real-life data from an evaluation of India's National Health Insurance Program. Our methodology enables us to identify new active causal effects that were not identified in past analyses. Ultimately, our simulation studies and case study demonstrate how our methodology can yield more informative analyses in modern experiments with interference, treatment nonadherence, missing outcomes, and complicated outcome generation mechanisms.