Haokun Lu, Xiaojun Song
arXiv 19 Oct 2026 · Econometrics
arXiv:2610.25986 · PDF · Extracted main text
This paper develops tuning-free tests for treatment-effect heterogeneity. By characterizing the null hypothesis as constancy of the expectile treatment-effect curve, we construct test statistics as continuous functionals of the mean-centered sample expectile process. We establish a uniform Bahadur representation, derive the asymptotic properties of the test statistics under the null and fixed alternatives, and show nontrivial power against local alternatives approaching the null at the $n^{-1/2}$ rate. For feasible inference, we develop a fast multiplier bootstrap that consistently approximates the nonpivotal null distribution while capturing the first-order effect of estimating the unknown common shift. The procedure is tuning-free, requiring no density or score-function estimation, bandwidth selection, or repeated refitting. The framework further incorporates a pointwise-studentized refinement and a covariate adjustment that reduces asymptotic variation without altering the population discrepancy. Monte Carlo comparisons with existing methods show generally reliable size control, competitive power, and substantial computational savings. An application to a large-scale water-conservation experiment illustrates the proposed tests.
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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 | Roger Koenker and Zhijie Xiao (2002) Inference on the quantile regression process | 1.000 | 5 | 3 | 100% |
| 2 | Whitney K. Newey and James L. Powell (1987) Asymmetric least squares estimation and testing | 1.000 | 5 | 3 | 100% |
| 3 | Peng Ding, Avi Feller, and Luke Miratrix (2016) Randomization inference for treatment effect variation | 0.928 | 4 | 4 | 100% |
| 4 | Victor Chernozhukov and Iván Fernández-Val (2005) Subsampling inference on quantile regression processes | 0.928 | 4 | 3 | 100% |
| 5 | EunYi Chung and Mauricio Olivares (2021) Permutation test for heterogeneous treatment effects with a nuisance parameter | 0.928 | 4 | 3 | 100% |
| 6 | EunYi Chung and Mauricio Olivares (2025) Quantile-based test for heterogeneous treatment effects | 0.928 | 4 | 3 | 100% |
| 7 | Paul J Ferraro and Michael K Price (2013) Using nonpecuniary strategies to influence behavior: Evidence from a large-scale field experiment | 0.874 | 7 | 2 | 100% |
| 8 | Hajo Holzmann and Bernhard Klar (2016) Expectile asymptotics | 0.644 | 2 | 2 | 100% |
| 9 | Tatsushi Oka, Shota Yasui, Yuta Hayakawa, and Undral Byambadalai (2026) Regression adjustment for estimating distributional treatment effects in randomized controlled trials | 0.585 | 3 | 1 | 100% |
| 10 | Paul J Ferraro and Juan José Miranda (2013) Heterogeneous treatment effects and mechanisms in information-based environmental policies: Evidence from a large-scale field ex… | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 32 scored citations.