Optimal Screening in Experiments with Partial Compliance

TL;DR

This paper explores optimal experimental design with partial compliance, showing that screening out non-compliers while retaining compliers maximizes power and minimizes bias, with practical strategies proposed for unobserved compliance.

Contribution

It introduces a theoretically optimal screening method in experiments with partial compliance, balancing bias reduction and power maximization, and discusses feasible implementation strategies.

Findings

Screening out non-compliers improves statistical power.

Retaining all compliers aligns LATE with standard 2SLS.

Proposed a simple test for screening efficacy.

Abstract

This note studies optimal experimental design under partial compliance when experimenters can screen participants prior to randomization. Theoretical results show that retaining all compliers and screening out all non-compliers achieves three complementary aims: (i) the Local Average Treatment Effect is the same as the standard 2SLS estimator with no screening; (ii) median bias is minimized; and (iii) statistical power is maximized. In practice, complier status is unobserved. We therefore discuss feasible screening strategies and propose a simple test for screening efficacy. Future work will conduct an experiment to demonstrate the feasibility and advantages of the optimal screening design.