This episode explores modern nonlinear regression methods crucial for predictive inference in causal analysis. It focuses on tree-based techniques like regression trees, random forests, and boosted trees, as well as neural networks and deep learning. The text discusses the theoretical guarantees of these methods, particularly concerning their approximation quality and convergence rates under various sparsity assumptions. Finally, it provides a practical case study using wage data to compare the predictive performance of these algorithms and introduces the concept of ensemble learning and automated machine learning (AutoML) frameworks for combining predictions.
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