ManySpace
Türkçe Sign in Sign up

AI & MLSpecializedAIML-CAU-007

Heterogeneous Effects and Machine Learning for Causal Estimation

Using flexible ML models to estimate effects that vary by individual, with orthogonalisation to avoid bias.

Difficulty
5 / 5
Importance
3 / 5
Study time
1–2 h
Kind of work
Mixed

Why it matters

The learner can explain how double machine learning removes regularisation bias and interpret a conditional average treatment effect.

The route to this topic

Route AIML-CAU-007
Natural Numbers What Is a Model? Variables & Constants Learning from Data Tasks: Regression, Classification, and Beyond Features and Targets Correlation vs Causation Datasets and Splits Classification Framing Linear Regression I: Prediction and Residuals Prediction versus Causation Scores and Logits Decision Trees Loss and Fitting: Squared Error Potential Outcomes and Treatment Effects Sigmoid and Probabilities Linear Regression II: Least Squares Fitting Overfitting and Underfitting Logistic Regression Propensity Scores and Inverse Probability Weighting Gradient Boosting Heterogeneous Effects and Machine Learning for Causal Estimation