Julia Hatamyar, Noemi Kreif, Rudi Rocha, Martin Huber
arXiv 18 Oct 2023 · Econometrics
arXiv:2310.11962 · PDF · DOI · OpenAlex · Extracted main text
We combine two recently proposed nonparametric difference-in-differences methods, extending them to enable the examination of treatment effect heterogeneity in the staggered adoption setting using machine learning. The proposed method, machine learning difference-in-differences (MLDID), allows for estimation of time-varying conditional average treatment effects on the treated, which can be used to conduct detailed inference on drivers of treatment effect heterogeneity. We perform simulations to evaluate the performance of MLDID and find that it accurately identifies the true predictors of treatment effect heterogeneity. We then use MLDID to evaluate the heterogeneous impacts of Brazil's Family Health Program on infant mortality, and find those in poverty and urban locations experienced the impact of the policy more quickly than other subgroups.
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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, Sant’Anna, Pedro HC (2021) Difference-in-differences with multiple time periods | 1.000 | 19 | 5 | 100% |
| 2 | Lu, Chen, Nie, Xinkun, Wager, Stefan (2019) Robust nonparametric difference-in-differences estimation | 1.000 | 14 | 3 | 100% |
| 3 | Zimmert, Michael (2020) Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding | 0.928 | 4 | 3 | 100% |
| 4 | Chang, Neng-Chieh (2020) Double/debiased machine learning for difference-in-differences models | 0.644 | 2 | 2 | 100% |
| 5 | Chernozhukov, Victor, Demirer, Mert, Duflo, Esther, Fernandez-Val, I… (2018) Generic machine learning inference on heterogeneous treatment effects in randomized experiments, with an application to immuniza… | 0.644 | 2 | 2 | 100% |
| 6 | Gavrilova, Evelina, Zoutman, Floris (2023) Dynamic Causal Forests, with an Application to Payroll Tax Incidence in Norway | 0.644 | 2 | 2 | 100% |
| 7 | Bhalotra, Sonia R, Rocha, Rudi, Soares, Rodrigo R (2019) Does universalization of healthwork? Evidence from health systems restructuring and expansion in Brazil self | 0.585 | 3 | 1 | 100% |
| 8 | Hone, Thomas, Saraceni, Valeria, Medina Coeli, Claudia, Trajman, Ane… (2020) Primary healthcare expansion and mortality in Brazil’s urban poor: A cohort analysis of 1.2 million adults | 0.585 | 3 | 1 | 100% |
| 9 | Hone, Thomas, Rasella, Davide, Barreto, Mauricio L, Majeed, Azeem, M… (2017) Association between expansion of primary healthcare and racial inequalities in mortality amenable to primary care in Brazil: A n… | 0.511 | 2 | 1 | 100% |
| 10 | Macinko, James, Harris, Matthew J (2015) Brazil’s family health strategy—delivering community-based primary care in a universal health system | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 33 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A Fixed-Effects Causal Forest for Staggered Adoption, with an Application to Medicaid Expansion | 1.000 | 5 | 4 |
| 2 | 2606.24785 | 0.405 | 1 | 1 |