Working Papers:
10. Vira Semenova.
Adaptive Estimation of Aggregated Values of Conditional Linear Programs. NEW VERSION
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