Detailed course description:
- ML for Group Average Treatment Effects, Conditional Average Treatment Effects
- causal forest
- double-lasso
- double machine learning
- partialling-out, partially linear regression, interactive regression model, IV model
- overfitting, regularisation bias
- neyman-orthogonality, sample-splitting
- hyperparameter optimisation
- sensitivity analysis
- quantile treatment effects
- mediation analysis
- sample selection models, dynamic treatment effects
- cluster robust DML
- double machine learning for DiD
- policy trees
- empirical bayes
- bayesian networks
- estimating CATE from satellite images and other kind of images
- Large language models for ATE estimation
Practical classes are taught in R, Python and Matlab. Participants are asked to install the software before the course.
Schedule:
21.01.2025 - 23.01.2025
15:00-21:30 GMT+1(Warsaw, Madrid, Berlin, Rome)
9:00-15:30 EST (Boston, New York, Washington)
18 hours contact hours