Orthogonal Machine Learning for Demand Estimation: High Dimensional Causal Inference in Dynamic Panels

This work extends the Double ML framework to allow for a number of treatments that may grow with the sample size and the analysis of panel data under sequentially exogenous errors, and shows that second stage LASSO and debiased LassO have asymptotic properties equivalent to oracle estimators with no upstream error.