Jianhua Mei, Fu Ouyang, Thomas T. Yang
arXiv 30 Jul 2025 · Econometrics
arXiv:2507.22312 · PDF · Extracted main text
We propose a novel and computationally efficient approach for nonparametric conditional density estimation in high-dimensional settings that achieves dimension reduction without imposing restrictive distributional or functional form assumptions. To uncover the underlying sparsity structure of the data, we develop an innovative conditional dependence measure and a modified cross-validation procedure that enables data-driven variable selection, thereby circumventing the need for subjective threshold selection. We demonstrate the practical utility of our dimension-reduced conditional density estimation by applying it to doubly robust estimators for average treatment effects. Notably, our proposed procedure is able to select relevant variables for nonparametric propensity score estimation and also inherently reduce the dimensionality of outcome regressions through a refined ignorability condition. We evaluate the finite-sample properties of our approach through comprehensive simulation studies and an empirical study on the effects of 401(k) eligibility on savings using SIPP data.
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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 | Farrell, M. H., T. Liang, and S. Misra (2021) Deep neural networks for estimation and inference | 1.000 | 6 | 3 | 100% |
| 2 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 5 | 3 | 100% |
| 3 | Wang, X., W. Pan, W. Hu, Y. Tian, and H. Zhang (2015) Conditional distance correlation | 0.928 | 4 | 4 | 100% |
| 4 | Azadkia, M. and S. Chatterjee (2021) A simple measure of conditional dependence | 0.928 | 4 | 4 | 100% |
| 5 | Hall, P., J. Racine, and Q. Li (2004) Cross-validation and the estimation of conditional probability densities | 0.804 | 25 | 6 | 52% |
| 6 | Mai, Q. and H. Zou (2015) The fused Kolmogorov filter: A nonparametric model-free screening method | 0.737 | 3 | 2 | 100% |
| 7 | Chernozhukov, V., C. Hansen, N. Kallus, M. Spindler, and V. Syrgkanis (2024) Applied causal inference powered by ML and AI | 0.737 | 3 | 2 | 100% |
| 8 | Farrell, M (2015) Robust inference on average treatment effects with possibly more covariates than observations | 0.737 | 3 | 2 | 100% |
| 9 | Gelber, A. M (2011) How do 401 (k) s Affect Saving? Evidence from Changes in 401 (k) Eligibility | 0.693 | 13 | 1 | 100% |
| 10 | Benjamin, D. J (2003) Does 401 (k) eligibility increase saving? Evidence from propensity score subclassification | 0.693 | 6 | 1 | 100% |
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