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Found 7 papers, 1 papers with code
Adaptive Estimation of Intersection Bounds: a Classification Approach
This paper studies averages of intersection bounds -- the bounds defined by the infimum of a collection of regression functions -- and other similar functionals of these bounds, such as averages of saddle values.
Generalized Lee Bounds
Lee (2009) is a common approach to bound the average causal effect in the presence of selection bias, assuming the treatment effect on selection has the same sign for all subjects.
Inference on weighted average value function in high-dimensional state space
Second, we give a correction term for the transition density of the state variable.
Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models
This paper proposes a Lasso-type estimator for a high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR).
Estimation and Inference on Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels under Weak Dependence
The first step of our procedure is orthogonalization, where we partial out the controls and unit effects from the outcome and the base treatment and take the cross-fitted residuals.
Debiased Machine Learning of Set-Identified Linear Models
This paper provides estimation and inference methods for an identified set's boundary (i. e., support function) where the selection among a very large number of covariates is based on modern regularized tools.
Debiased Machine Learning of Conditional Average Treatment Effects and Other Causal Functions
This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning (ML) tools.
