Clément de Chaisemartin, Xavier D'Haultfœuille
arXiv 11 Aug 2025 · Econometrics
arXiv:2508.07808 · PDF · DOI · OpenAlex · Extracted main text
This paper considers the identification of dynamic treatment effects with panel data, in complex designs where the treatment may not be binary and may not be absorbing. We first show that under no-anticipation and parallel-trends assumptions, we can identify event-study effects comparing outcomes under the actual treatment path and under the status-quo path where all units would have kept their period-one treatment throughout the panel. Those effects can be helpful to evaluate ex-post the policies that effectively took place, and once properly normalized they estimate weighted averages of marginal effects of the current and lagged treatments on the outcome. Yet, they may still be hard to interpret, and they cannot be used to evaluate the effects of other policies than the ones that were conducted. To make progress, we impose another restriction, namely a random coefficients distributed-lag linear model, where effects remain constant over time. Under this model, the usual distributed-lag two-way-fixed-effects regression may be misleading. Instead, we show that this random coefficients model can be estimated simply. We illustrate our findings by revisiting Gentzkow, Shapiro and Sinkinson (2011).
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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 | de Chaisemartin, C. and X. D'Haultfuille (2025) Difference-in-differences estimators of intertemporal treatment effects | 1.000 | 9 | 4 | 100% |
| 2 | Gentzkow, M., J. M. Shapiro, and M. Sinkinson (2011) The effect of newspaper entry and exit on electoral politics | 1.000 | 5 | 4 | 100% |
| 3 | Callaway, B. and P. H. Sant'Anna (2021) Difference-in-differences with multiple time periods | 0.874 | 6 | 2 | 100% |
| 4 | Sun, L. and S. Abraham (2021) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 0.843 | 3 | 3 | 100% |
| 5 | Arellano, M. and S. Bonhomme (2012) Identifying distributional characteristics in random coefficients panel data models | 0.811 | 4 | 2 | 100% |
| 6 | Borusyak, K., X. Jaravel, and J. Spiess (2024) Revisiting event-study designs: robust and efficient estimation | 0.811 | 4 | 2 | 100% |
| 7 | de Chaisemartin, C., X. D'Haultfuille, F. Pasquier, D. Sow, and G. V… (2022) Difference-in-differences for continuous treatments and instruments with stayers | 0.811 | 4 | 2 | 100% |
| 8 | Graham, B. S. and J. L. Powell (2012) Identification and estimation of average partial effects in “irregular” correlated random coefficient panel data models | 0.811 | 4 | 2 | 100% |
| 9 | de Chaisemartin, C. and X. D'Haultfuille (2020) Two-way fixed effects estimators with heterogeneous treatment effects | 0.737 | 3 | 2 | 100% |
| 10 | Liu, L., Y. Wang, and Y. Xu (2024) A practical guide to counterfactual estimators for causal inference with time-series cross-sectional data | 0.644 | 4 | 1 | 100% |
Showing the top 10 of 34 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Difference-in-Differences with Time-varying Continuous Treatments Using Double/Debiased Machine Learning | 0.405 | 1 | 1 |