arXiv 16 Jan 2023 · Econometrics
arXiv:2301.06283 · PDF · DOI · OpenAlex · Extracted main text
Plausible identification of conditional average treatment effects (CATEs) may rely on controlling for a large number of variables to account for confounding factors. In these high-dimensional settings, estimation of the CATE requires estimating first-stage models whose consistency relies on correctly specifying their parametric forms. While doubly-robust estimators of the CATE exist, inference procedures based on the second stage CATE estimator are not doubly-robust. Using the popular augmented inverse propensity weighting signal, we propose an estimator for the CATE whose resulting Wald-type confidence intervals are doubly-robust. We assume a logistic model for the propensity score and a linear model for the outcome regression, and estimate the parameters of these models using an $\ell_1$ (Lasso) penalty to address the high dimensional covariates. Our proposed estimator remains consistent at the nonparametric rate and our proposed pointwise and uniform confidence intervals remain asymptotically valid even if one of the logistic propensity score or linear outcome regression models are misspecified. These results are obtained under similar conditions to existing analyses in the high-dimensional and nonparametric literatures.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Semenova, V. and V. Chernozhukov (2021, 08) (2021) Debiased machine learning of conditional average treatment effects and other causal functions | 0.920 | 9 | 6 | 78% |
| 2 | Tan, Z (2020) Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data | 0.874 | 5 | 2 | 100% |
| 3 | Belloni, A., V. Chernozhukov, D. Chetverikov, and K. Kato (2015) Some new asymptotic theory for least squares series: Pointwise and uniform results | 0.855 | 16 | 5 | 62% |
| 4 | Newey, W (1997) Convergence rates and asymptotic normality for series estimators | 0.843 | 4 | 4 | 75% |
| 5 | Chetverikov, D. and J. R.-V. Sørensen (2021) Analytic and bootstrap-after-cross-validation methods for selecting penalty parameters of high-dimensional m-estimators | 0.822 | 9 | 5 | 56% |
| 6 | Bickel, P. J., Y. Ritov, and A. B. Tsybakov (2009) Simultaneous analysis of Lasso and Dantzig selector | 0.737 | 3 | 2 | 100% |
| 7 | Zimmert, M. and M. Lechner (2019) Nonparametric estimation of causal heterogeneity under high-dimensional confounding | 0.693 | 5 | 1 | 100% |
| 8 | Belloni, A. and V. Chernozhukov (2013) Least squares after model selection in high-dimensional sparse models | 0.644 | 2 | 2 | 100% |
| 9 | Wang, W. and J. Yan (2021) Shape-restricted regression splines with R package splines2 | 0.644 | 2 | 2 | 100% |
| 10 | Tan, Z (2017) Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data | 0.511 | 3 | 2 | 33% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Uniform Confidence Band for Marginal Treatment Effect Function | 0.405 | 1 | 1 |