Potential Outcome Rankings for Counterfactual Decision Making
Kawakami, Yuta Tian, Jin
Kawakami, Yuta
Tian, Jin
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Machine Learning
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Conference proceeding
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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.
Citation
Y. Kawakami, J. Tian, "Potential Outcome Rankings for Counterfactual Decision Making," 2026, pp. 36679-36687.
Source
Proceedings of the AAAI Conference on Artificial Intelligence
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AAAI Conference on Artificial Intelligence
Keywords
38 Economics, 3801 Applied Economics, 46 Information and Computing Sciences, 49 Mathematical Sciences
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AAAI Conference on Artificial Intelligence
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Association for the Advancement of Artificial Intelligence