Brantly Callaway, Emmanuel Selorm Tsyawo
arXiv 5 Aug 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2308.02899 · PDF · DOI · OpenAlex · Extracted main text
This paper considers identifying and estimating causal effect parameters in a staggered treatment adoption setting -- that is, where a researcher has access to panel data and treatment timing varies across units. We consider the case where untreated potential outcomes may follow non-parallel trends over time across groups. This implies that the identifying assumptions of leading approaches such as difference-in-differences do not hold. We mainly focus on the case where untreated potential outcomes are generated by an interactive fixed effects model and show that variation in treatment timing provides additional moment conditions that can be used to recover a large class of target causal effect parameters. Our approach exploits the variation in treatment timing without requiring either (i) a large number of time periods or (ii) requiring any extra exclusion restrictions. This is in contrast to essentially all of the literature on interactive fixed effects models which requires at least one of these extra conditions. Rather, our approach directly applies in settings where there is variation in treatment timing. Although our main focus is on a model with interactive fixed effects, our idea of using variation in treatment timing to recover causal effect parameters is quite general and could be adapted to other settings with non-parallel trends across groups such as dynamic panel data models.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Callaway, Brantly, Karami, Sonia (2023) Treatment effects in interactive fixed effects models with a small number of time periods self | 1.000 | 21 | 3 | 100% |
| 2 | Callaway, Brantly, Sant'Anna, Pedro HC (2021) Difference-in-differences with multiple time periods self | 0.874 | 5 | 2 | 100% |
| 3 | Brown, Nicholas, Butts, Kyle (2022) A unified framework for dynamic treatment effect estimation in interactive fixed effect models | 0.811 | 4 | 2 | 100% |
| 4 | Sun, Liyang, Abraham, Sarah (2021) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 0.811 | 4 | 2 | 100% |
| 5 | Ahn, Seung C, Lee, Young H, Schmidt, Peter (2013) Panel data models with multiple time-varying individual effects | 0.737 | 3 | 2 | 100% |
| 6 | (2020) Two-way fixed effects estimators with heterogeneous treatment effects | 0.737 | 3 | 2 | 100% |
| 7 | Gobillon, Laurent, Magnac, Thierry (2016) Regional policy evaluation: Interactive fixed effects and synthetic controls | 0.737 | 3 | 2 | 100% |
| 8 | Imbens, Guido, Kallus, Nathan, Mao, Xiaojie (2021) Controlling for unmeasured confounding in panel data using minimal bridge functions: From two-way fixed effects to factor models | 0.737 | 3 | 2 | 100% |
| 9 | Xu, Yiqing (2017) Generalized synthetic control method: Causal inference with interactive fixed effects models | 0.737 | 3 | 2 | 100% |
| 10 | Bertrand, Marianne, Duflo, Esther, Mullainathan, Sendhil (2004) How much should we trust differences-in-differences estimates? | 0.644 | 2 | 2 | 100% |
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