Treatment Effect Quantiles in Stratified Randomized Experiments and Matched Observational Studies

TL;DR

This paper develops a distribution-free, robust method for inferring quantiles of individual treatment effects in stratified experiments and observational studies, addressing heavy tails and outliers.

Contribution

It introduces a novel, computationally efficient greedy algorithm for valid quantile inference, extending to observational studies with sensitivity analysis.

Findings

Valid p-values for quantile effects are achievable with the proposed algorithm.

The method is robust to heavy tails and outliers in treatment effects.

Inference is valid for all quantiles simultaneously, providing comprehensive effect insights.

Abstract

Evaluating the treatment effects has become an important topic for many applications. However, most existing literature focuses mainly on the average treatment effects. When the individual effects are heavy-tailed or have outlier values, not only may the average effect not be appropriate for summarizing the treatment effects, but also the conventional inference for it can be sensitive and possibly invalid due to poor large-sample approximations. In this paper we focus on quantiles of individual effects, which can be more robust measures of treatment effects in the presence of extreme individual effects. Moreover, our inference for quantiles of individual effects are purely randomization-based, which avoids any distributional assumption on the units. We first consider inference for stratified randomized experiments, extending the recent work of Caughey et al. (2021). The calculation of valid $p$-values for testing null hypotheses on quantiles of individual effects involves linear integer programming, which is generally NP hard. To overcome this issue, we propose a greedy algorithm with a certain optimal transformation, which has much lower computational cost, still leads to valid $p$-values and is less conservative than the usual relaxation by dropping the integer constraint. We then extend our approach to matched observational studies and propose sensitivity analysis to investigate to what extent our inference on quantiles of individual effects is robust to unmeasured confounding. Both the randomization inference and sensitivity analysis are simultaneously valid for all quantiles of individual effects, which are actually free lunches added to the conventional analysis assuming constant effects. Furthermore, the inference results can be easily visualized and interpreted.