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Causal Models for Longitudinal and Panel Data: A Survey

Author

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  • Dmitry Arkhangelsky
  • Guido Imbens

Abstract

In this survey we discuss the recent causal panel data literature. This recent literature has focused on credibly estimating causal effects of binary interventions in settings with longitudinal data, emphasizing practical advice for empirical researchers. It pays particular attention to heterogeneity in the causal effects, often in situations where few units are treated and with particular structures on the assignment pattern. The literature has extended earlier work on difference-in-differences or two-way-fixed-effect estimators. It has more generally incorporated factor models or interactive fixed effects. It has also developed novel methods using synthetic control approaches.

Suggested Citation

  • Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," NBER Working Papers 31942, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:31942
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    Cited by:

    1. Yiqing Xu & Anqi Zhao & Peng Ding, 2024. "Factorial Difference-in-Differences," Papers 2407.11937, arXiv.org, revised Aug 2024.
    2. Dmitry Arkhangelsky & Aleksei Samkov, 2024. "Sequential Synthetic Difference in Differences," Papers 2404.00164, arXiv.org.
    3. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences With Staggered Adoptions," Papers 2312.05985, arXiv.org, revised Oct 2024.

    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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