11:30 am–12:30 pm
Jones 303 5747 S. Ellis Ave.
Xinran Li
Department of Statistics
University of Chicago
Title: Randomization Inference with Sample Attrition
Abstract: Sample attrition, or equivalently missing outcomes, is a common challenge in randomized controlled experiments, and analyses that simply discard units with missing outcomes may suffer from severe bias. In this talk, we develop computationally efficient methods for randomization inference that remain valid under a broad class of potentially informative missingness mechanisms, allowing a unit’s missingness to depend on its potential outcomes. We pay particular attention to monotone missingness mechanisms, under which outcomes are systematically more likely to be observed under treatment than under control, or vice versa. Such restrictions arise naturally in many empirical settings, especially in the social sciences.
Specifically, we construct valid p-values for testing sharp, bounded, and quantile null hypotheses on treatment effects through a worst-case consideration of the classical Fisher randomization test. By leveraging distribution-free test statistics, these worst-case p-values can be computed efficiently and often admit closed-form solutions. Importantly, by incorporating both potential outcomes and potential missingness indicators into the test statistic, our methods can exploit structural assumptions such as monotone missingness to sharpen inference. Our approach also connects to a range of partial-identification bounds in the literature. We illustrate the proposed methods through simulation studies and empirical applications evaluating the impacts of job training, media censorship, and artificial intelligence.