Alexandre Belloni, Victor Chernozhukov, Ivan Fernández-Val, Christian Hansen
arXiv 11 Nov 2013 · Mathematics — Statistics Theory · publishedEconometrica (2017) · 304 citations (OpenAlex)
arXiv:1311.2645 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function-valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post-regularization and post-selection inference that are uniformly valid (honest) across a wide-range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets.
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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 | Belloni, A., V. Chernozhukov, and C. Hansen (2014) a): Inference on Treatment Effects After Selection Amongst High-Dimensional Controls self | 1.000 | 12 | 5 | 100% |
| 2 | Belloni, A., V. Chernozhukov, and C. Hansen (2013) a): Inference for High-Dimensional Sparse Econometric Models self | 1.000 | 5 | 3 | 100% |
| 3 | Chernozhukov, V. and C. Hansen (2004) The impact of 401(k) participation on the wealth distribution: An instrumental quantile regression analysis self | 0.950 | 14 | 3 | 86% |
| 4 | Benjamin, D. J (2003) Does 401(k) eligibility increase saving? Evidence from propensity score subclassification | 0.928 | 5 | 3 | 80% |
| 5 | Abadie, A (2003) Semiparametric Instrumental Variable Estimation of Treatment Response Models | 0.920 | 9 | 4 | 78% |
| 6 | Belloni, A., D. Chen, V. Chernozhukov, and C. Hansen (2012) Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain self | 0.909 | 8 | 4 | 75% |
| 7 | Belloni, A. and V. Chernozhukov (2011) $_1$-Penalized Quantile Regression for High Dimensional Sparse Models self | 0.843 | 5 | 4 | 60% |
| 8 | Chernozhukov, V. and C. Hansen (2006) Instrumental quantile regression inference for structural and treatment effect models self | 0.843 | 3 | 3 | 100% |
| 9 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, a… (2016) Double Machine Learning for Treatment and Causal Parameters self | 0.811 | 4 | 2 | 100% |
| 10 | Belloni, A. and V. Chernozhukov (2013) Least Squares After Model Selection in High-dimensional Sparse Models self | 0.737 | 4 | 3 | 50% |
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