Victor Chernozhukov is an econometrician (Professor of Economics, MIT) known for foundational work on high-dimensional and machine-learning-based econometrics, quantile and instrumental-variable methods, and causal inference — most influentially the double/debiased machine learning framework.
Key Contributions / Features
Double/debiased machine learning (Chernozhukov et al. 2018): the Neyman-orthogonal-score + cross-fitting framework for n-valid inference on causal parameters with ML nuisance estimation (Double Machine Learning); coauthor of the DoubleML R package paper.
Extensive contributions to high-dimensional/lasso econometrics, instrumental-variable and quantile regression, and inference after model selection.