Program evaluation with high-dimensional data

By Alexandre Belloni, Victor Chernozhukov, Iv'an Fern'andez-Val, Christian Hansen. 2013

95 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

The State of Applied Econometrics - Causality and Policy Evaluation (2016, positive): “Belloni et al. [2013] propose a modification of the LASSO that addresses these concerns and restores the ability of LASSO to produce valid causal estimates. They propose a double selection procedure, where they use LASSO first to select covariates that are correlated with the outcome, and then again”
Learning When-to-Treat Policies (2019, background): “There is also a growing number of existing works that have applied orthogonal moments construction to policy evaluation (Belloni et al., 2017; Chernozhukov et al., 2016b; Kallus, 2018).”
Efficient Policy Learning (2017, methodology): “There is a recent, active, literature that seeks to use tools from optimization and machine learning to develop semiparametrically efficient average treatment effect estimation with improved finite sample performance: examples include double machine learning (Belloni et al., 2017; Chernozhukov et al”
A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS (2014, methodology): “These same moment conditions were also used in recent papers by Belloni et al. (2014, 2015) and Farrell (2015) for treatment evaluation with high-dimensional data, including settings with more covariates than observations. The discussion so far has focused on doubly robust estimators that require th”
Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels. (2017, background): “Further extensions of orthogonality for the functional parameter, such as distributional regression (Belloni et al. (2013), Belloni et al. (2019)) or support function in set-identified models (Semenova (2018)), do not nest our heterogeneous treatment effects framework.”
Robust Estimation of Causal Effects via High-Dimensional Covariate Balancing Propensity Score. (2018, result): “This result agrees with the theory of AIPW estimators in Belloni et al. (2013a) and Farrell (2015).”
Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models (2013, methodology): “The uniform validity of l1-penalized methods for a continuum of problems (indexed by T in our case) has been established for quantile regression in [2] and for least squares in [10].”
Lasso adjustments of treatment effect estimates in randomized experiments (2015, methodology): “…method for selecting adjustment variables via a two-stage procedure which concatenates features from models for treatment and outcome, and similarly, [17] gives very general results for estimating a wide range of treatment eﬀect parameters, including the case of instrumental variables estimation.”
Double/debiased machine learning for treatment and structural parameters (2017, unknown): “In the partially linear model, the DML approach complements Belloni et al. (2013), Zhang and Zhang (2014), van de Geer et al. (2014), Javanmard and Montanari (2014b), and Belloni et al. (2014), Belloni et al. (2014), and Belloni et al. (2015), all of which consider estimation and inference for…”
Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments (2015, methodology): “In their basic results, Berry et al. (1995) use five variables in xit: a constant, an air conditioning dummy, horsepower divided by weight, miles per dollar, and vehicle size.”
Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach (2015, unknown): “This estimator and related methods such as those in Candès and Tao (2007), Meinshausen and Yu (2009), Bickel et al.”
Econometric Methods for Program Evaluation (2018, unknown): “Belloni et al. (2014) and Farrell (2015) develop new program evaluation methods employing machinery from the high-dimensional literature in statistics.”