Introduction to Classical ML and Its Application in Econometric Research

In this talk, I first introduce several basic concepts related to classical machine learning algorithms, such as overfitting, regularization, and adaptive boosting. I also showcase how to implement adaptive boosting in the Julia programming language to provide a more concrete understanding of how it works. Then, I move on to introduce a labor economics paper that utilizes machine learning algorithms to predict the treatment and control groups of the minimum wage policy based on demographic characteristics. Finally, I demonstrate how I employ this two-stage framework, which uses various machine learning algorithms to predict treatment and control groups used in the next stage of causal inference on Taiwanese data.