Potential Outcome Rankings for Counterfactual Decision Making

2025-11-13

AI Summary

This paper addresses counterfactual decision-making under uncertainty by proposing a novel individualized decision paradigm grounded in the ranking of potential outcomes. Departing from conventional expected utility maximization, we define and identify two new causal decision metrics: Probability of Ranking (PoR) and Probability of Best outcome realization (PoB). We establish nonparametric identification theorems for both metrics and derive estimable bounds. We further develop a finite-sample estimator leveraging double robustness and sensitivity analysis, accompanied by theoretical guarantees on convergence rates. Numerical experiments demonstrate the estimator’s superiority in bias reduction, variance control, and robustness to model misspecification. Empirical evaluation on real-world clinical decision data shows that PoR and PoB significantly enhance the stability and interpretability of personalized treatment recommendations. Collectively, this work provides a theoretically grounded, generalizable framework for causal decision support systems.

Technology Category

• Reasoning under Uncertainty: Decision/Utility Theory
• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Counterfactual decision-making in the face of uncertainty involves selecting the optimal action from several alternatives using causal reasoning. Decision-makers often rank expected potential outcomes (or their corresponding utility and desirability) to compare the preferences of candidate actions. In this paper, we study new counterfactual decision-making rules by introducing two new metrics: the probabilities of potential outcome ranking (PoR) and the probability of achieving the best potential outcome (PoB). PoR reveals the most probable ranking of potential outcomes for an individual, and PoB indicates the action most likely to yield the top-ranked outcome for an individual. We then establish identification theorems and derive bounds for these metrics, and present estimation methods. Finally, we perform numerical experiments to illustrate the finite-sample properties of the estimators and demonstrate their application to a real-world dataset.

Problem

• Develops new counterfactual decision rules using outcome ranking probabilities
• Establishes identification theorems and bounds for novel ranking metrics
• Presents estimation methods validated through numerical experiments

Innovation

• Introduces potential outcome ranking probabilities as new metric
• Establishes identification theorems and bounds for metrics
• Presents estimation methods with numerical validation