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Found 5 papers, 0 papers with code
Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning
We suggest double/debiased machine learning estimators of direct and indirect quantile treatment effects under a selection-on-observables assumption.
Inference for ROC Curves Based on Estimated Predictive Indices
We provide a comprehensive theory of conducting in-sample statistical inference about receiver operating characteristic (ROC) curves that are based on predicted values from a first stage model with estimated parameters (such as a logit regression).
Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data
While most treatment evaluations focus on binary interventions, a growing literature also considers continuously distributed treatments.
Estimation of Conditional Average Treatment Effects with High-Dimensional Data
In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size.
Testing for Unobserved Heterogeneous Treatment Effects with Observational Data
Unobserved heterogeneous treatment effects have been emphasized in the recent policy evaluation literature (see e. g., Heckman and Vytlacil, 2005).
