Conformal Inference for Experimental Attrition in Social Science Research

TL;DR

This paper introduces a robust statistical method for handling attrition in social science experiments, producing more reliable treatment effect estimates even with missing data.

Contribution

It presents a novel approach combining recent inference techniques with missing data tools to improve causal inference under attrition.

Findings

The method achieves better coverage than existing approaches.

It produces narrower, more precise prediction intervals.

Reanalysis of published studies demonstrates practical utility.

Abstract

Attrition in survey and field experiments presents a challenge for social science research. Common approaches to deal with this problem -- such as complete case analysis, multiple imputation, and weighting methods -- rely on strong assumptions that may not hold in practice. This paper introduces a new method that combines recent advances in statistical inference with established tools for handling missing data. The approach produces prediction intervals for treatment effects that are both robust and precise. Evidence from simulation studies shows that the method achieves better coverage and produces narrower intervals than common alternatives. The reanalysis of two recently published experiment studies illustrates how this framework allows researchers to compare treatment effects across participants who remain in the study, those who drop out, and the full sample. Taken together, these results highlight how the proposed approach provides a stronger foundation for causal inference in the presence of attrition.