Recent Developments in Causal Inference and Machine Learning

Jennie E. Brand, Xiang Zhou, Yu Xie

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations

Abstract

This article reviews recent advances in causal inference relevant to sociology. We focus on a selective subset of contributions aligning with four broad topics: causal effect identification and estimation in general, causal effect heterogeneity, causal effect mediation, and temporal and spatial interference. We describe how machine learning, as an estimation strategy, can be effectively combined with causal inference, which has been traditionally concerned with identification. The incorporation of machine learning in causal inference enables researchers to better address potential biases in estimating causal effects and uncover heterogeneous causal effects. Uncovering sources of effect heterogeneity is key for generalizing to populations beyond those under study. While sociology has long emphasized the importance of causal mechanisms, historical and life-cycle variation, and social contexts involving network interactions, recent conceptual and computational advances facilitate more principled estimation of causal effects under these settings. We encourage sociologists to incorporate these insights into their empirical research.

Original languageEnglish (US)
Pages (from-to)81-110
Number of pages30
JournalAnnual Review of Sociology
Volume49
DOIs
StatePublished - Jul 31 2023

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science

Keywords

  • causal inference
  • counterfactuals
  • external validit
  • extrapolation
  • machine learning
  • mediation
  • treatment effect heterogeneity

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