Causal Inference and Machine Learning for Reliable Decision-Making
TL;DR
The methodological bridge connecting causal reasoning with modern supervised learning is reviewed and why correlation-driven models give biased answers to questions about actions is explained.
Abstract
Machine learning systems trained to predict outcomes from observational data often fail when their outputs guide interventions. A model that accurately forecasts who will churn, default, or recover does not by itself reveal what happens if a policy changes the treatment assigned to those people. This gap between prediction and intervention is the central concern of causal inference. This paper reviews the methodological bridge connecting causal reasoning with modern supervised learning and explains why correlation-driven models give biased answers to questions about actions. The potential outcomes framework and structural causal models are introduced as complementary languages for stating the target of estimation. Identification is treated through directed acyclic graphs, the backdoor criterion, and instrumental variables, which specify when an effect can be recovered from data. The discussion then turns to estimation of average and conditional average treatment effects using meta-learners (S, T, and X), causal forests, and double or debiased machine learning, together with uplift modeling for targeting decisions. A synthetic benchmark illustrates typical error behavior: orthogonalized and forest-based estimators reach an absolute ATE bias near 0.05 and lower CATE error than a single-model baseline. Applications in pricing, marketing, healthcare, and public policy are surveyed, along with validation difficulties that arise because counterfactual outcomes are never observed. The paper closes with the assumptions, chiefly unconfoundedness and overlap, on which every reported number depends.