mlsanityData to 7 Oct 2026

Mind the Gap: Optimal and Equitable Encouragement Policies

arXiv 2309.07176 ↗First posted 12 Sep 2023cs.LG stat.MLcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 74 references that are in the map. First posted on arXiv 12 Sep 2023.

Builds on

24 of 74 references.

References · 24

  1. 2018A Reductions Approach to Fair Classificationcited by 585
  2. 2014Taming the Monster: A Fast and Simple Algorithm for Contextual Banditscited by 200
  3. 2019Orthogonal Statistical Learningcited by 150
  4. 2016A vector-contraction inequality for Rademacher complexitiescited by 141
  5. 2019Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combinationcited by 79
  6. 2020A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scorescited by 74
  7. 2018Confounding-Robust Policy Improvementcited by 73
  8. 2021It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarkscited by 61
  9. 2019Counterfactual Risk Assessments, Evaluation, and Fairnesscited by 59
  10. 2018Interval Estimation of Individual-Level Causal Effects Under Unobserved Confoundingcited by 51
  11. 2020Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Oddscited by 40
  12. 2015Counterfactual Risk Minimization: Learning from Logged Bandit Feedbackcited by 33
12 more
  1. 2021An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraintscited by 29
  2. 2019Semi-Parametric Efficient Policy Learning with Continuous Actionscited by 26
  3. 2020Algorithmic Risk Assessments Can Alter Human Decision-Making Processes in High-Stakes Government Contextscited by 22
  4. 2020Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessmentcited by 18
  5. 2020Fairness, Welfare, and Equity in Personalized Pricingcited by 17
  6. 2021Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessmentcited by 10
  7. 2020Multiply robust estimation of causal effects under principal ignorabilitycited by 9
  8. 2021Treatment Allocation under Uncertain Costscited by 9
  9. 2022"If it didn't happen, why would I change my decision?": How Judges Respond to Counterfactual Explanations for the Public Safety Assessmentcited by 6
  10. 2019Efficient estimation of optimal regimes under a no direct effect assumptioncited by 5
  11. 2023Doubly Robust Counterfactual Classificationcited by 5
  12. 2023Popular Support for Balancing Equity and Efficiency in Resource Allocation: A Case Study in Online Advertising to Increase Welfare Program Awarenesscited by 3

Built on by

Not cited yet.