De-biasing Treatment Effects with Double Machine Learning

This post explores the integration of causal reasoning into machine learning models and focuses on de-biasing treatment effects with Double Machine Learning (DML). It explains what Average Treatment Effects (ATE) are and the challenges of estimating ATE using Linear Regression. The post then introduces Double Machine Learning and its use in estimating ATE. It provides a comparison between Linear Regression and DML, showing that DML provides less biased estimates. The post also mentions other causal methods such as Propensity score matching, S-Learner, T-Learner, Doubly-Robust Learner, and Instrument variable learner.