Abstract

Causal inference is foundational for reliable data‑driven decision making because it seeks to uncover and quantify cause–effect relationships from observational and experimental data. Traditional statistical techniques focus on association rather than causation; however, many real‑world decisions require understanding what would happen under alternative interventions rather than what is merely correlated. Causal inference techniques—including randomized controlled experiments, propensity score methods, structural equation models, instrumental variables, difference‑in‑differences, regression discontinuity, and graphical causal models (e.g., Pearl’s do‑calculus and causal Bayesian networks)—enable researchers and practitioners to estimate treatment effects and predict outcomes under hypothetical interventions. Advances in machine learning have further enhanced causal discovery, counterfactual estimation, and heterogeneous treatment effect modeling. This paper reviews major causal inference frameworks, their assumptions, methodological developments, and applications in economics, medicine, social sciences, and policy analysis. We discuss identification strategies, estimation techniques, and diagnostic checks necessary for valid causal claims. Challenges such as confounding, selection bias, model misspecification, and data limitations are addressed. Through empirical examples and simulation studies, we illustrate how causal methods support robust decision making in complex domains. Finally, we outline future research directions, including scalable causal discovery from high‑dimensional data, integration with reinforcement learning, and interpretable counterfactual modeling.