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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.
We propose a generalization of the linear panel quantile regression model to accommodate both textit{sparse} and textit{dense} parts: sparse means while the number of covariates available is large, potentially only a much smaller number of them have
Quantile regression has become a valuable tool to analyze heterogeneous covaraite-response associations that are often encountered in practice. The development of quantile regression methodology for high-dimensional covariates primarily focuses on ex
The widespread use of quantile regression methods depends crucially on the existence of fast algorithms. Despite numerous algorithmic improvements, the computation time is still non-negligible because researchers often estimate many quantile regressi
This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome va
With the availability of high dimensional genetic biomarkers, it is of interest to identify heterogeneous effects of these predictors on patients survival, along with proper statistical inference. Censored quantile regression has emerged as a powerfu