Causal Machine Learning: A Deductive–Inductive Framework for Sociological Research

Abstract Causal explanation is central to sociological research, shaping both theoretical development and empirical inquiry. This paper argues that causal machine learning—which integrates deductive identification strategies with inductive estimation techniques—offers an analytical approach for modeling complex, nonlinear social processes within the potential outcomes framework. We argue that causal machine learning operates through an iterative feedback loop: Theoretical assumptions guide flexible estimation, which inductively uncovers complex heterogeneities and nonlinearities, and these discoveries subsequently refine and expand sociological knowledge. Drawing on a systematic review of recent sociological research (2014–2024), we highlight how causal machine learning is advancing work in three key areas: causal effect heterogeneity, causal mediation analysis, and time-varying causal inference. These developments expand the methodological tool kit available to sociologists and strengthen the discipline’s ability to test, refine, and extend theories of social explanation. We conclude by outlining emerging directions, including high-dimensional causal inference and generative artificial intelligence, that are opening new methodological frontiers in causal machine learning for sociology.