Mind the Gap: Optimal and Equitable Encouragement Policies

TL;DR

This paper develops a framework for designing optimal and equitable encouragement policies in settings where individuals choose whether to follow treatment recommendations, emphasizing fairness and robustness under constraints.

Contribution

It introduces a model distinguishing responsiveness and treatment efficacy, providing tractable policy characterizations and fairness targets in recommendation-based decision problems.

Findings

Policy value depends on responsiveness and treatment efficacy.

Induced treatment take-up is the fairness target, not recommendation rates.

Case studies demonstrate the application to SNAP benefits and pretrial monitoring.

Abstract

In consequential domains, it is often impossible to compel individuals to take treatment, so that optimal policy rules are merely suggestions in the presence of human non-adherence to treatment recommendations. We study personalized decision problems in which the planner controls recommendations into treatment rather than treatment itself. Under a covariate-conditional no-direct-effect model of encouragement, policy value depends on two distinct objects: responsiveness to encouragement and treatment efficacy. This modeling distinction makes induced treatment take-up, rather than recommendation rates alone, the natural fairness target and yields tractable policy characterizations under budget and access constraints. In settings with deterministic algorithmic recommendations, the same model localizes overlap-robustness to the recommendation-response model rather than the downstream outcome model. We illustrate the methods in case studies based on data from reminders of SNAP benefits recertification, and from pretrial supervised release with electronic monitoring. While the specific remedy to inequities in algorithmic allocation is context-specific, it requires studying both take-up of decisions and downstream outcomes of them.