Identification-robust inference for the LATE with high-dimensional covariates ...
This paper investigates the local average treatment effect (LATE) with high-dimensional covariates, irrespective of the strength of identification. We propose a novel test statistic for the high-dimensional LATE, demonstrating that our test has uniformly correct asymptotic size. By employing the dou...
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| Format: | Report |
| Language: | unknown |
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arXiv
2023
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| Online Access: | https://dx.doi.org/10.48550/arxiv.2302.09756 https://arxiv.org/abs/2302.09756 |
| Summary: | This paper investigates the local average treatment effect (LATE) with high-dimensional covariates, irrespective of the strength of identification. We propose a novel test statistic for the high-dimensional LATE, demonstrating that our test has uniformly correct asymptotic size. By employing the double/debiased machine learning (DML) method to estimate nuisance parameters, we develop easy-to-implement algorithms for inference and confidence interval calculation of the high-dimensional LATE. Simulations indicate that our test is robust against both weak identification and high-dimensional setting concerning size control and power performance, outperforming other conventional tests. Applying the proposed test to railroad and population data to study the effect of railroad access on urban population growth, we observe the shorter length of confidence intervals and smaller point estimates for the railroad access coefficients compared to the conventional tests. ... : 45pages, 2 figures ... |
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