Juan Carlos Escanciano, Telmo Pérez-Izquierdo
arXiv 25 Jan 2023 · Econometrics
arXiv:2301.10643 · PDF · DOI · OpenAlex · Extracted main text
Many parameters of interest in economics and other social sciences depend on generated regressors. Examples in economics include structural parameters in models with endogenous variables estimated by control functions and in models with sample selection, treatment effect estimation with propensity score matching, and marginal treatment effects. More recently, Machine Learning (ML) generated regressors are becoming ubiquitous for these and other applications such as imputation with missing regressors, dimension reduction, including autoencoders, learned proxies, confounders and treatments, and for feature engineering with unstructured data, among others. We provide the first general method for valid inference with regressors generated from ML. Inference with generated regressors is complicated by the very complex expression for influence functions and asymptotic variances. Additionally, ML-generated regressors may lead to large biases in downstream inferences. To address these problems, we propose Automatic Locally Robust/debiased GMM estimators in a general three-step setting with ML-generated regressors. We illustrate our results with treatment effects and counterfactual parameters in the partially linear and nonparametric models with ML-generated regressors. We provide sufficient conditions for the asymptotic normality of our debiased GMM estimators and investigate their finite-sample performance through Monte Carlo simulations.
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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 | Newey, Whitney K (1994) The asymptotic variance of semiparametric estimators | 1.000 | 8 | 4 | 100% |
| 2 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 8 | 3 | 100% |
| 3 | Heckman, James J and Ichimura, Hidehiko and Todd, Petra (1998) Matching as an econometric evaluation estimator | 1.000 | 8 | 3 | 100% |
| 4 | Imbens, Guido W and Newey, Whitney K (2009) Identification and estimation of triangular simultaneous equations models without additivity | 0.928 | 4 | 3 | 100% |
| 5 | Chernozhukov, Victor and Newey, Whitney K and Singh, Rahul (2022) Automatic debiased machine learning of causal and structural effects | 0.920 | 9 | 3 | 78% |
| 6 | Chernozhukov, Victor and Escanciano, Juan Carlos and Ichimura, Hideh… (2022) Locally robust semiparametric estimation self | 0.904 | 23 | 8 | 74% |
| 7 | Hahn, Jinyong and Ridder, Geert (2013) Machine learning predictions as regression covariates | 0.884 | 29 | 7 | 69% |
| 8 | Mammen, Enno and Rothe, Christoph and Schienle, Melanie (2016) Semiparametric estimation with generated covariates | 0.874 | 5 | 2 | 100% |
| 9 | Ichimura, Hidehiko and Newey, Whitney K (2022) The influence function of semiparametric estimators | 0.754 | 7 | 4 | 43% |
| 10 | Bengio, Yoshua and Courville, Aaron and Vincent, Pascal (2013) Representation learning: A review and new perspectives | 0.737 | 3 | 3 | 67% |
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