Causal inference and machine learning

This course explores modern methods at the intersection of machine learning 🤖 and causal inference 🔍, equipping students with both predictive and explanatory tools for high-dimensional data. It covers:

• High-dimensional linear models 📊 for prediction and inference
• Modern nonlinear methods 🌳 such as random forests and deep neural networks
• Randomized controlled trials (RCTs) 🎲 as the gold standard for causal estimation
• Causal DAGs 🔗 for representing and reasoning about causal relationships
• Double/debiased machine learning ⚖️ for valid inference after ML model selection
• Heterogeneous treatment effect estimation 🔀 using causal trees and forests
• Feature engineering with deep learning 🛠️ for causal and predictive modeling

Discord Syllabus

Second Semester (August - December) 2025
Thrusday 7:30-9:20 | Friday 10.30-12:20
Class Meeting: JOIN
Laboratory Meeting: JOIN
Microsoft Teams

Links of Material
Course Lecture
Laboratoy Material
Causal ML Book