A Review of Causal Decision Making

TL;DR

This review comprehensively discusses causal decision-making, covering causal structure learning, effect estimation, policy application, challenges, recent advances, and future directions, with a Python toolkit for implementation.

Contribution

It consolidates various causal decision-making methods into a unified framework and provides practical guidance and a Python toolkit for real-world applications.

Findings

Identifies key challenges in causal decision-making.

Summarizes recent advances overcoming these challenges.

Provides a Python-based collection of methods for practitioners.

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.