This episode explores advanced econometric methods for causal inference using Double/Debiased Machine Learning (DML). It focuses on applying DML to instrumental variable (IV) models, including partially linear IV models and interactive IV regression models (IRM) for estimating Local Average Treatment Effects (LATE). A significant portion addresses robust DML inference under weak identification, a common challenge where instruments provide limited information about the endogenous variable. The chapter revisits classic examples like the effect of institutions on economic growth and 401(k) participation on financial assets, demonstrating how DML can offer more robust and flexible analyses compared to traditional methods, especially in the presence of weak instruments.
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