arXiv 15 Oct 2024 · Econometrics
arXiv:2410.11408 · PDF · DOI · OpenAlex · Extracted main text
Uncovering the heterogeneous effects of particular policies or "treatments" is a key concern for researchers and policymakers. A common approach is to report average treatment effects across subgroups based on observable covariates. However, the choice of subgroups is crucial as it poses the risk of $p$-hacking and requires balancing interpretability with granularity. This paper proposes a nonparametric approach to construct heterogeneous subgroups. The approach enables a flexible exploration of the trade-off between interpretability and the discovery of more granular heterogeneity by constructing a sequence of nested groupings, each with an optimality property. By integrating our approach with "honesty" and debiased machine learning, we provide valid inference about the average treatment effect of each group. We validate the proposed methodology through an empirical Monte-Carlo study and apply it to revisit the impact of maternal smoking on birth weight, revealing systematic heterogeneity driven by parental and birth-related characteristics.
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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 | Athey, Susan, Imbens, Guido W (2016) Recursive partitioning for heterogeneous causal effects | 1.000 | 16 | 4 | 100% |
| 2 | Semenova, Vira, Chernozhukov, Victor (2021) Debiased machine learning of conditional average treatment effects and other causal functions | 1.000 | 9 | 3 | 100% |
| 3 | Almond, Douglas, Chay, Kenneth Y, Lee, David S (2005) The costs of low birth weight | 1.000 | 7 | 4 | 100% |
| 4 | Athey, Susan, Tibshirani, Julie, Wager, Stefan (2019) Generalized random forests | 1.000 | 6 | 3 | 100% |
| 5 | Cattaneo, Matias D (2010) Efficient semiparametric estimation of multi-valued treatment effects under ignorability | 1.000 | 5 | 4 | 100% |
| 6 | Lee, Kwonsang, Small, Dylan S, Dominici, Francesca (2021) Discovering heterogeneous exposure effects using randomization inference in air pollution studies | 1.000 | 5 | 4 | 100% |
| 7 | Bargagli-Stoffi, Falco J, De Witte, Kristof, Gnecco, Giorgio (2022) Heterogeneous causal effects with imperfect compliance: A Bayesian machine learning approach | 0.928 | 4 | 3 | 100% |
| 8 | Fan, Qingliang, Hsu, Yu-Chin, Lieli, Robert P, Zhang, Yichong (2022) Estimation of conditional average treatment effects with high-dimensional data | 0.928 | 4 | 3 | 100% |
| 9 | Abrevaya, Jason, Hsu, Yu-Chin, Lieli, Robert P (2015) Estimating conditional average treatment effects | 0.874 | 5 | 2 | 100% |
| 10 | Breiman, L., Friedman, J. H., Olshen, R. A., Stone, C. J (1984) Classification and Regression Trees | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 53 scored citations.