4 citations · 8 across the 7 of their papers we have counts for
10 papers
Debiased Inference for Bounding Wage Inequality with Many Controls
Yaroslav Korobka, Vira Semenova
We study estimation and inference for a partially identified parameter whose identified set depends on a first-stage nuisance parameter that must itself be estimated. Combining the…
Adaptive Estimation of Aggregated Values of Conditional Linear Programs
Gevorg Khandamiryan, Vira Semenova
We develop a covariate-assisted approach to partially identified parameters that are solutions to an under-identified system of linear equations with known coefficients. Examples i…
On the Lower Confidence Band for the Optimal Welfare in Policy Learning
Kirill Ponomarev, Vira Semenova
We study inference on the optimal welfare in a policy learning problem and propose reporting a lower confidence band (LCB). A natural approach to constructing an LCB is to invert a…
Debiased Machine Learning of Aggregated Intersection Bounds and Other Causal Parameters
Vira Semenova
This paper proposes a novel framework of aggregated intersection of regression functions, where the target parameter is obtained by averaging the minimum (or maximum) of a collecti…
Generalized Lee Bounds
Vira Semenova
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.…
Welfare Analysis in Dynamic Models
Victor Chernozhukov, Whitney Newey, Vira Semenova
This paper introduces metrics for welfare analysis in dynamic models. We develop estimation and inference for these parameters even in the presence of a high-dimensional state spac…
Machine Learning for Dynamic Discrete Choice
Vira Semenova
Dynamic discrete choice models often discretize the state vector and restrict its dimension in order to achieve valid inference. I propose a novel two-stage estimator for the set-i…
Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models
Denis Nekipelov, Vira Semenova, Vasilis Syrgkanis
This paper proposes a Lasso-type estimator for a high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR). In addition to this parameter,…
Debiased Machine Learning of Set-Identified Linear Models
Vira Semenova
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…
Debiased Machine Learning of Conditional Average Treatment Effects and Other Causal Functions
Vira Semenova, Victor Chernozhukov
This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment ef…