Shunsuke Imai, Lei Qin, Takahide Yanagi
arXiv 3 May 2023 · Econometrics · publishedJournal of Business and Economic Statistics (2025)
arXiv:2305.02185 · PDF · DOI · OpenAlex · Extracted main text
We consider a panel data analysis to examine the heterogeneity in treatment effects with respect to groups, periods, and a pre-treatment covariate of interest in the staggered difference-in-differences setting of Callaway and Sant'Anna (2021). Under standard identification conditions, a doubly robust estimand conditional on the covariate identifies the group-time conditional average treatment effect given the covariate. Focusing on the case of a continuous covariate, we propose a three-step estimation procedure based on nonparametric local polynomial regressions and parametric estimation methods. Using uniformly valid distributional approximation results for empirical processes and weighted/multiplier bootstrapping, we develop doubly robust inference methods to construct uniform confidence bands for the group-time conditional average treatment effect function and a variety of useful summary parameters. The accompanying R package didhetero allows for easy implementation of our methods.
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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 | Lee, S., Okui, R., and Whang, Y.J (2017) Doubly robust uniform confidence band for the conditional average treatment effect function | 0.923 | 14 | 3 | 79% |
| 2 | Chernozhukov, V., Chetverikov, D., and Kato, K (2014) a | 0.894 | 7 | 3 | 71% |
| 3 | Fan, Q., Hsu, Y.C., Lieli, R.P., and Zhang, Y (2022) Estimation of conditional average treatment effects with high-dimensional data | 0.874 | 10 | 2 | 100% |
| 4 | Mammen, E (1993) Bootstrap and wild bootstrap for high dimensional linear models | 0.874 | 6 | 3 | 67% |
| 5 | Callaway, B. and Sant'Anna, P.H (2021) Difference-in-differences with multiple time periods | 0.838 | 34 | 11 | 59% |
| 6 | Fan, J. and Gijbels, I (1996) Local polynomial modelling and its applications | 0.644 | 2 | 2 | 100% |
| 7 | Chernozhukov, V., Chetverikov, D., and Kato, K (2014) b | 0.585 | 10 | 4 | 20% |
| 8 | Cattaneo, M.D., Chandak, R., Jansson, M., and Ma, X (2024) Boundary adaptive local polynomial conditional density estimators | 0.585 | 3 | 1 | 100% |
| 9 | Piterbarg, V.I (1996) Asymptotic methods in the theory of Gaussian processes and fields | 0.511 | 3 | 2 | 33% |
| 10 | Ghosal, S., Sen, A., and van der Vaart, A.W (2000) Testing monotonicity of regression | 0.511 | 2 | 2 | 50% |
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