Trustworthy Policy Learning Under the Counterfactual No-Harm Criterion

EasyChair Preprint 13093

Abstract

Trustworthy policy learning has significant importance in making reliable and harmless treatment decisions for individuals. Previous policy learning approaches aim at the well-being of subgroups by maximizing the utility function (e.g., conditional average causal effects, post-view click-through&conversion rate in recommendations), however, individual-level counterfactual no-harm criterion has rarely been discussed. In this paper, we first formalize the counterfactual no-harm criterion for policy learning from a principal stratification perspective. Next, we propose a novel upper bound for the fraction negatively affected by the policy and show the consistency and asymptotic normality of the estimator. Based on the estimators for the policy utility and harm upper bounds, we further propose a policy learning approach that satisfies the counterfactual no-harm criterion, and prove its consistency to the optimal policy reward for parametric and non-parametric policy classes, respectively. Extensive experiments are conducted to show the effectiveness of the proposed policy learning approach for satisfying the counterfactual no-harm criterion.

Keyphrases: Individual treatment effects, Trustworthy AI, causal inference, policy learning

@booklet{EasyChair:13093,
  author    = {Haoxuan Li and Chunyuan Zheng and Yixiao Cao and Zhi Geng and Yue Liu and Peng Wu},
  title     = {Trustworthy Policy Learning Under the Counterfactual No-Harm Criterion},
  howpublished = {EasyChair Preprint 13093},
  year      = {EasyChair, 2024}}