Orthogonal Statistical Learning
Explore orthogonal statistical learning in this 45-minute lecture by Vasilis Syrgkanis from Microsoft Research, presented at the Simons Institute. Delve into non-asymptotic excess risk guarantees for statistical learning with unknown nuisance parameters. Examine a two-stage sample splitting meta-algorithm and its performance under Neyman orthogonality conditions. Discover how this approach enables the use of existing statistical learning and machine learning results for new guarantees. Learn about oracle rates achievement, specific estimation algorithms, and applications in heterogeneous treatment effect estimation, offline policy optimization, domain adaptation, and learning with missing data. Investigate topics such as automated outlier removal, generalized method of moments, IV regression, neural network and forest heuristic improvements, and multi-tasking applications in 401k and gasoline demand analysis.