arXiv 14 Jul 2026 · Econometrics
arXiv:2607.12622 · PDF · DOI · OpenAlex · Extracted main text
We propose a nonparametric integrated conditional moment (ICM) test for treatment effect heterogeneity across subpopulations defined by a given covariate subvector. Under unconfoundedness, the null is recast as a conditional moment restriction based on a Neyman-orthogonal score, which reduces the first-order sensitivity of the empirical process to nuisance parameter estimation. The test statistics are constructed as continuous functionals of a marked empirical process. We establish a uniform feasible-to-oracle approximation and derive the asymptotic properties of these test statistics under the null and fixed alternatives. We further show that the test has nontrivial power against local alternatives converging to the null at the $n^{-1/2}$ rate, and develop an easy-to-implement multiplier bootstrap for feasible inference. We also develop extensions to tests of parametric CATE specifications and to settings with endogenous treatment and a binary instrument. Finally, we apply the proposed testing approach to study whether the effect of maternal smoking during pregnancy on infant birth weight varies with maternal age.
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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 | Zongwu Cai, Ying Fang, Ming Lin, and Shengfang Tang (2025) A nonparametric test of heterogeneity in conditional quantile treatment effects | 1.000 | 7 | 3 | 100% |
| 2 | Yu-Chin Hsu (2017) Consistent tests for conditional treatment effects | 0.928 | 5 | 3 | 80% |
| 3 | Jason Abrevaya, Yu-Chin Hsu, and Robert P Lieli (2015) Estimating conditional average treatment effects | 0.874 | 8 | 2 | 100% |
| 4 | Sokbae Lee, Ryo Okui, and Yoon-Jae Whang (2017) Doubly robust uniform confidence band for the conditional average treatment effect function | 0.874 | 5 | 2 | 100% |
| 5 | Keisuke Hirano, Guido W Imbens, and Geert Ridder (2003) Efficient estimation of average treatment effects using the estimated propensity score | 0.744 | 17 | 4 | 41% |
| 6 | Richard K Crump, V Joseph Hotz, Guido W Imbens, and Oscar A Mitnik (2008) Nonparametric tests for treatment effect heterogeneity | 0.737 | 3 | 2 | 100% |
| 7 | Qingliang Fan, Yu-Chin Hsu, Robert P Lieli, and Yichong Zhang (2022) Estimation of conditional average treatment effects with high-dimensional data | 0.737 | 3 | 2 | 100% |
| 8 | Guido Imbens and Joshua Angrist (1994) Identification and estimation of local average treatment effects | 0.737 | 3 | 2 | 100% |
| 9 | Pedro HC Sant’Anna (2021) Nonparametric tests for treatment effect heterogeneity with duration outcomes | 0.737 | 3 | 2 | 100% |
| 10 | Heejung Bang and James M Robins (2005) Doubly robust estimation in missing data and causal inference models | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 41 scored citations.