Publication Type

Working Paper

Version

acceptedVersion

Publication Date

10-2020

Abstract

Credible counterfactual analysis requires high-dimensional controls. This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data. We propose a novel doubly robust score for double/debiased estimation and inference for the unconditional quantile regression (Firpo, Fortin, and Lemieux, 2009) as a measure of heterogeneous counterfactual marginal effects. We propose a multiplier bootstrap inference for the Lasso double/debiased estimator, and develop asymptotic theories to guarantee that the bootstrap works. Simulation studies support our theories. Applying the proposed method to Job Corps survey data, we find that i) marginal effects of counterfactually extending the duration of the exposure to the Job Corps program are globally positive across quantiles regardless of definitions of the treatment and outcome variables, and that ii) these counterfactual effects are larger for higher potential earners than lower potential earners regardless of whether we define the outcome as the level or its logarithm.

Keywords

counterfactual analysis, double/debiased machine learning, doubly robust score

Discipline

Econometrics

Research Areas

Econometrics

First Page

1

Last Page

45

Copyright Owner and License

Authors

Comments

Submitted to journal

Included in

Econometrics Commons

Share

COinS