Asymmetric Counterfactual Utility

Asymmetric counterfactual utility focuses on optimizing decision-making by considering the potential outcomes under all possible actions, not just the observed outcome. Current research explores methods for learning optimal policies under these asymmetric utilities, employing techniques like deep deterministic policy gradients and online learning algorithms with adjustments for incentive compatibility, often addressing the challenge of unidentifiable utility functions through partial identification and minimax decision rules. This framework has significant implications for various fields, including healthcare (e.g., treatment decisions) and resource allocation (e.g., demand response), where understanding the potential consequences of different choices is crucial for effective policy design.