Working Papers:
10. Vira Semenova.
Adaptive Estimation of Aggregated Values of Conditional Linear Programs. NEW VERSION R&R, Review of Economics and Statistics
9. Kirill Ponomarev and Vira Semenova.
On the Lower Confidence Band for the Optimal Welfare in Policy Learning. NEW VERSION! [arXiv]
8. Victor Chernozhukov, Whitney K. Newey, and Vira Semenova.
Welfare Analysis in Dynamic Models. [arXiv]
(formerly titled: Inference on Weighted Average Value Function in High-Dimensional State Space)
7. Vira Semenova.
Debiased Machine Learning of Aggregated Intersection Bounds and Other Causal Parameters. [arXiv]
6. Vira Semenova.
Machine Learning for Dynamic Discrete Choice. [arxiv]
Published and Forthcoming Work:
Vira Semenova and Victor Chernozhukov.
Debiased Machine Learning for Conditional Average Treatment Effects and Other Causal Functions.
The Econometrics Journal, 24(2), May 2021, 264–289.
DOI | arXiv | Kaggle Notebook | CATE for the Treated (Kaggle notebook)
Denis Nekipelov, Vira Semenova, and Vasilis Syrgkanis.
Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models.
The Econometrics Journal, 24(2), May 2021, 330–354. DOI | arXiv
Vira Semenova, Matthew Goldman, Victor Chernozhukov, and Matt Taddy.
Inference on Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels with Weak Dependence.
Quantitative Economics, 14(2), May 2023, 471–510. arXiv | R Code | Kaggle Notebook
Vira Semenova.
Debiased Machine Learning of Set-Identified Linear Models.
The Journal of Econometrics, 235(2), August 2023, 1725–1746. arXiv | R Code
Vira Semenova.
Generalized Lee Bounds.
The Journal of Econometrics, forthcoming. arXiv | R code