Potential Outcome Rankings for Counterfactual Decision Making
This work addresses decision-making challenges in causal inference for researchers and practitioners, though it appears incremental as it builds on existing causal reasoning frameworks.
The paper tackles the problem of counterfactual decision-making under uncertainty by introducing two new metrics, PoR and PoB, to rank potential outcomes and identify actions likely to yield top outcomes, with results validated through numerical experiments and a real-world dataset application.
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.