With Applications in Python, R, and Stata
The book delves into the essential theoretical concepts of supervised machine learning while showcasing diverse applications through Python, R, and Stata. It strikes a balance between theoretical foundations and practical implementations, enhancing readers' understanding of machine learning techniques across various software platforms.
Advanced micro-econometric techniques are explored in this comprehensive guide aimed at enhancing evidence-based program evaluation in social sciences. The author provides a detailed toolbox for conducting rigorous ex-post evaluations using Stata, covering methods like regression-adjustment, matching, and synthetic control. Each technique is accompanied by a statistical overview and practical examples using real and simulated data, equipping readers with the knowledge to select and apply appropriate methods tailored to specific policy contexts.