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Annual Review of Statistics and Its Application

Volume 11, 2024

Causal Inference in the Social Sciences

Abstract

Knowledge of causal effects is of great importance to decision makers in a wide variety of settings. In many cases, however, these causal effects are not known to the decision makers and need to be estimated from data. This fundamental problem has been known and studied for many years in many disciplines. In the past thirty years, however, the amount of empirical as well as methodological research in this area has increased dramatically, and so has its scope. It has become more interdisciplinary, and the focus has been more specifically on methods for credibly estimating causal effects in a wide range of both experimental and observational settings. This work has greatly impacted empirical work in the social and biomedical sciences. In this article, I review some of this work and discuss open questions.

Keyword(s): causal inference, difference-in-differences, double robustness, experiments, instrumental variables, observational studies, regression discontinuity, synthetic controls, unconfoundedness
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2024-04-22
2024-05-11
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Causal Inference in the Social Sciences
Annual Review of Statistics and Its Application 11, 123 (2024); https://doi.org/10.1146/annurev-statistics-033121-114601
/content/journals/10.1146/annurev-statistics-033121-114601
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