Yukun Ma
arXiv 20 Feb 2023 · Econometrics
arXiv:2302.09756 · PDF · Extracted main text
This paper presents an inference method for the local average treatment effect (LATE) in the presence of high-dimensional covariates, irrespective of the strength of identification. We propose a novel high-dimensional conditional test statistic with uniformly correct asymptotic size. We provide an easy-to-implement algorithm to infer the high-dimensional LATE by inverting our test statistic and employing the double/debiased machine learning method. Simulations indicate that our test is robust against both weak identification and high dimensionality concerning size control and power performance, outperforming other conventional tests. Applying the proposed method to railroad and population data to study the effect of railroad access on urban population growth, we observe that our methodology yields confidence intervals that are 49% to 92% shorter than conventional results, depending on specifications.
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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 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters | 0.923 | 14 | 6 | 79% |
| 2 | Hornung, Erik (2015) Railroads and growth in Prussia | 0.874 | 10 | 2 | 100% |
| 3 | Ambrus, Attila and Field, Erica and Gonzalez, Robert (2020) Loss in the time of cholera: Long-run impact of a disease epidemic on the urban landscape | 0.874 | 6 | 2 | 100% |
| 4 | Belloni, Alexandre and Chernozhukov, Victor and Fernandez-Val, Ivan… (2017) Program evaluation and causal inference with high-dimensional data | 0.794 | 6 | 4 | 50% |
| 5 | Lee, David S and McCrary, Justin and Moreira, Marcelo J and Porter,… (2022) Valid t-ratio inference for IV | 0.737 | 3 | 2 | 100% |
| 6 | Stock, James H and Wright, Jonathan H (2000) GMM with weak identification | 0.737 | 3 | 2 | 100% |
| 7 | Mikusheva, Anna and Sun, Liyang (2022) Inference with many weak instruments | 0.644 | 2 | 2 | 100% |
| 8 | Tan, Zhiqiang (2006) Regression and weighting methods for causal inference using instrumental variables | 0.644 | 2 | 2 | 100% |
| 9 | Andrews, Isaiah and Mikusheva, Anna (2016) Conditional inference with a functional nuisance parameter | 0.585 | 10 | 3 | 20% |
| 10 | Angrist, Joshua D and Krueger, Alan B (1991) Does compulsory school attendance affect schooling and earnings? | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 57 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | An Introduction to Double/Debiased Machine Learning | 0.405 | 1 | 1 |
| 2 | Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters | 0.000 | 5 | 1 |