Potential Outcome Rankings for Counterfactual Decision Making
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingSimilar Papers 제목 키워드 기반
Statistical Decision Theory with Counterfactual Loss
Classical statistical decision theory evaluates treatment choices based solely on observed outcomes. However, by ignoring counterfactual outcomes, it cannot assess the quality of decisions relative to feasible alternativ…
counterfactualDistributional Counterfactual Explanations With Optimal Transport
Counterfactual explanations (CE) are the de facto method for providing insights into black-box decision-making models by identifying alternative inputs that lead to different outcomes. However, existing CE approaches, in…
counterfactualCounterfactual ExplanationDecision MakingConformal Counterfactual Inference under Hidden Confounding
Personalized decision making requires the knowledge of potential outcomes under different treatments, and confidence intervals about the potential outcomes further enrich this decision-making process and improve its reli…
Causal InferenceConformal PredictioncounterfactualCounterfactual Inference+2FLEX: Feature Importance from Layered Counterfactual Explanations
Machine learning models achieve state-of-the-art performance across domains, yet their lack of interpretability limits safe deployment in high-stakes settings. Counterfactual explanations are widely used to provide actio…
severity predictionFeature ImportanceMC3G: Model Agnostic Causally Constrained Counterfactual Generation
Machine learning models increasingly influence decisions in high-stakes settings such as finance, law and hiring, driving the need for transparent, interpretable outcomes. However, while explainable approaches can help u…