arXiv 1 Oct 2024 · Econometrics
arXiv:2410.00733 · PDF · DOI · OpenAlex · Extracted main text
Statistical inference of heterogeneous treatment effects (HTEs) across predefined subgroups is challenging when units interact because treatment effects may vary by pre-treatment variables, post-treatment exposure variables (that measure the exposure to other units' treatment statuses), or both. Thus, the conventional HTEs testing procedures may be invalid under interference. In this paper, I develop statistical methods to infer HTEs and disentangle the drivers of treatment effects heterogeneity in populations where units interact. Specifically, I incorporate clustered interference into the potential outcomes model and propose kernel-based test statistics for the null hypotheses of (i) no HTEs by treatment assignment (or post-treatment exposure variables) for all pre-treatment variables values and (ii) no HTEs by pre-treatment variables for all treatment assignment vectors. I recommend a multiple-testing algorithm to disentangle the source of heterogeneity in treatment effects. I prove the asymptotic properties of the proposed test statistics. Finally, I illustrate the application of the test procedures in an empirical setting using an experimental data set from a Chinese weather insurance program.
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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 | Chang, M., S. Lee, and Y.-J. Whang (2015) Nonparametric tests of conditional treatment effects with an application to single-sex schooling on academic achievements | 1.000 | 17 | 7 | 100% |
| 2 | Giné, E., D. M. Mason, and A. Y. Zaitsev (2003) The $$bm $$L$$ _$$mathbf $$1$$-norm density estimator process | 1.000 | 14 | 6 | 100% |
| 3 | Cai, J., A. De Janvry, and E. Sadoulet (2015) Social networks and the decision to insure | 0.874 | 6 | 2 | 100% |
| 4 | Crump, R. K., V. J. Hotz, G. Imbens, and O. A. Mitnik (2006) Nonparametric tests for treatment effect heterogeneity | 0.843 | 3 | 3 | 100% |
| 5 | Fan, Q., Y.-C. Hsu, R. P. Lieli, and Y. Zhang (2022) Estimation of conditional average treatment effects with high-dimensional data | 0.737 | 3 | 2 | 100% |
| 6 | Bargagli Stoffi, F., C. Tortú, and L. Forastiere (2020) Heterogeneous treatment and spillover effects under clustered network interference | 0.644 | 2 | 2 | 100% |
| 7 | Holm, S (1979) A simple sequentially rejective multiple test procedure | 0.644 | 2 | 2 | 100% |
| 8 | Lee, S., K. Song, and Y.-J. Whang (2013) Testing functional inequalities | 0.644 | 2 | 2 | 100% |
| 9 | Athey, S., D. Eckles, and G. W. Imbens (2018) Exact p-values for network interference | 0.585 | 3 | 1 | 100% |
| 10 | Manski, C. F (2013) Identification of treatment response with social interactions | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 36 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Randomization Inference of Heterogeneous Treatment Effects under Network Interference | 0.405 | 1 | 1 |