Recent Developments in Causal Inference and Machine Learning

crossref(2022)

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摘要
This paper provides an updated review of the latest advances in causal inference in sociology and other disciplines. We focus on four topics: causal effect identification and estimation in general, causal effect heterogeneity, causal effect mediation, and temporal and spatial interference. We show 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 the researcher to better address potential biases in estimating causal effects and uncover heterogeneous causal effects. Still, we caution that there is no panacea for causal inference, particularly with observational data. Suitable methods are appropriate only for particular research settings and valid only with unverifiable assumptions, often involving complicated causal pathways or situations with temporal or spatial interference. We also note that research settings that benefit from strong internal validity may have low external validity.
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