arXiv 15 Jun 2022 · Econometrics
arXiv:2206.07386 · PDF · DOI · OpenAlex · Extracted main text
Debiased machine learning (DML) offers an attractive way to estimate treatment effects in observational settings, where identification of causal parameters requires a conditional independence or unconfoundedness assumption, since it allows to control flexibly for a potentially very large number of covariates. This paper gives novel finite-sample guarantees for joint inference on high-dimensional DML, bounding how far the finite-sample distribution of the estimator is from its asymptotic Gaussian approximation. These guarantees are useful to applied researchers, as they are informative about how far off the coverage of joint confidence bands can be from the nominal level. There are many settings where high-dimensional causal parameters may be of interest, such as the ATE of many treatment profiles, or the ATE of a treatment on many outcomes. We also cover infinite-dimensional parameters, such as impacts on the entire marginal distribution of potential outcomes. The finite-sample guarantees in this paper complement the existing results on consistency and asymptotic normality of DML estimators, which are either asymptotic or treat only the one-dimensional case.
appendix boundary found by appendix_command · 67% of the source is main text. Read the extracted text to check this.
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 | Chernozhukov, Newey and Singh (2018) Automatic debiased machine learning of causal and structural effects | 1.000 | 7 | 3 | 100% |
| 2 | Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey and Robins (2018) Double/debiased Machine Learning for Treatment and Structural Parameters | 1.000 | 7 | 3 | 100% |
| 3 | Belloni, Chernozhukov, Fernández-Val and Hansen (2017) Program Evaluation and Causal Inference with High-dimensional Data | 1.000 | 6 | 3 | 100% |
| 4 | Belloni, Chernozhukov, Chetverikov and Wei (2018) Uniformly valid post-regularization confidence regions for many functional parameters in Z-estimation framework | 0.909 | 8 | 5 | 75% |
| 5 | Chernozhukov, Chetverikov and Koike (2021) Nearly optimal central limit theorem and bootstrap approximations in high dimensions | 0.874 | 6 | 4 | 67% |
| 6 | Chernozhukov, Newey and Singh (2021) A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees | 0.874 | 6 | 4 | 67% |
| 7 | Chernozhukov, Newey, Quintas-Martinez and Syrgkanis (2021) Automatic Debiased Machine Learning via Neural Nets for Generalized Linear Regression | 0.644 | 2 | 2 | 100% |
| 8 | Chernozhukov, Chetverikov and Kato (2014) Gaussian approximation of suprema of empirical processes | 0.585 | 4 | 3 | 25% |
| 9 | Athey and Imbens (2019) Machine Learning Methods That Economists Should Know About | 0.405 | 1 | 1 | 100% |
| 10 | Athey and Wager (2021) Policy Learning with Observational Data | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 22 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Policy Learning with Confidence$^$ | 0.644 | 2 | 2 |