arXiv 22 Jul 2026 · Econometrics
arXiv:2607.19644 · PDF · Extracted main text
Difference-in-differences with staggered adoption identifies group-time average treatment effects ATT(g,t) by comparing each cohort to units not yet treated, which avoids the "forbidden comparisons" that bias two-way fixed-effects estimators when effects are heterogeneous. This paper studies the covariate-conditional version of that object, tau_{g,t}(x), and estimates it with a fixed-effects causal forest. Within each (g,t) comparison block, the outcome and treatment are residualized on unit and period fixed effects inside each tree node, and honest causal trees split on treatment-effect heterogeneity in the covariates. The estimand is not new: Hatamyar, Kreif, Rocha and Huber (2023) introduced it using a doubly-robust R-learner, and Imai, Qin and Yanagi (2023) study it for a single continuous covariate. What we add is a different way to estimate it. Where those methods remove confounding by modeling nuisance functions, we remove it by differencing out unit and period effects within each tree node, following the fixed-effects residualization of Kattenberg, Scheer and Thiel (2023) and Gavrilova, Langorgen and Zoutman (2025) and carrying it into the Callaway-Sant'Anna group-time structure. In Monte Carlo experiments the estimator is the only forest-based method that stays unbiased and correctly covered for the overall effect under staggered timing with cohort-varying effects; two-way fixed effects and a pooled causal forest inherit large forbidden-comparison bias. We apply the method to the Callaway-Sant'Anna minimum-wage panel as a validation and to the staggered county-level rollout of the ACA Medicaid expansion, where it recovers an average 2.25 percentage-point fall in the uninsured rate and a conditional surface on which poorer and lower-income counties gained substantially more coverage -- heterogeneity measured along socioeconomic covariates that are not lags of the outcome.
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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 | Brantly Callaway and Pedro H. C. Sant'Anna (2021) Difference-in-differences with multiple time periods | 1.000 | 5 | 4 | 100% |
| 2 | Julia Hatamyar, Noemi Kreif, Rudi Rocha, and Martin Huber (2023) Machine learning for staggered difference-in-differences and dynamic treatment effect heterogeneity | 1.000 | 5 | 4 | 100% |
| 3 | Shunsuke Imai, Lei Qin, and Takahide Yanagi (2023) Doubly robust uniform confidence bands for group-time conditional average treatment effects in difference-in-differences | 1.000 | 5 | 4 | 100% |
| 4 | Evelina Gavrilova, Audun Langrgen, and Floris T. Zoutman (2025) Difference-in-difference causal forests, with an application to payroll tax incidence in norway | 1.000 | 5 | 3 | 100% |
| 5 | Stefan Wager and Susan Athey (2018) Estimation and inference of heterogeneous treatment effects using random forests | 0.811 | 4 | 2 | 100% |
| 6 | Susan Athey, Julie Tibshirani, and Stefan Wager (2019) Generalized random forests | 0.644 | 2 | 2 | 100% |
| 7 | Andrew Goodman-Bacon (2021) Difference-in-differences with variation in treatment timing | 0.644 | 2 | 2 | 100% |
| 8 | Mark Kattenberg, Bas Scheer, and Jurre Thiel (2023) Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences | 0.644 | 2 | 2 | 100% |
| 9 | Xiaomeng Lu, Xinkun Nie, and Stefan Wager (2019) Estimating individual treatment effects in difference-in-differences frameworks | 0.644 | 2 | 2 | 100% |
| 10 | Clément de Chaisemartin and Xavier D'Haultfuille (2020) Two-way fixed effects estimators with heterogeneous treatment effects | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 12 scored citations.