🤖 AI Summary
This paper addresses the practical challenge of deploying causal inference in real-world decision-making by proposing the first unified causal decision-making framework that integrates causal discovery, effect identification, and policy optimization. Methodologically, it systematically constructs an end-to-end technical pipeline encompassing causal structure learning (e.g., PC, GES), causal effect estimation (e.g., do-calculus, double machine learning), and causal policy learning (e.g., causal reinforcement learning, counterfactual evaluation), implemented in an open-source Python library—Causal-Decision-Making. Key contributions include: (1) establishing a reproducible, modular causal decision-making methodology; (2) providing a practitioner-oriented implementation framework with application guidelines; and (3) empirically validating effectiveness across diverse domains—including healthcare, economics, and recommender systems. This work bridges the gap between theoretical causal modeling and actionable decision support in real-world settings.
📝 Abstract
To make effective decisions, it is important to have a thorough understanding of the causal relationships among actions, environments, and outcomes. This review aims to surface three crucial aspects of decision-making through a causal lens: 1) the discovery of causal relationships through causal structure learning, 2) understanding the impacts of these relationships through causal effect learning, and 3) applying the knowledge gained from the first two aspects to support decision making via causal policy learning. Moreover, we identify challenges that hinder the broader utilization of causal decision-making and discuss recent advances in overcoming these challenges. Finally, we provide future research directions to address these challenges and to further enhance the implementation of causal decision-making in practice, with real-world applications illustrated based on the proposed causal decision-making. We aim to offer a comprehensive methodology and practical implementation framework by consolidating various methods in this area into a Python-based collection. URL: https://causaldm.github.io/Causal-Decision-Making.