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AI & MLRecommendedAIML-CAU-005

Propensity Scores and Inverse Probability Weighting

Adjusting observational data by modelling the probability of treatment.

Difficulty
4 / 5
Importance
4 / 5
Study time
1–2 h
Kind of work
Mixed

Why it matters

The learner can estimate propensity scores with logistic regression and compute an IPW effect estimate, noting the overlap assumption.

The route to this topic

Route AIML-CAU-005
Natural Numbers What Is a Model? Variables & Constants Learning from Data Tasks: Regression, Classification, and Beyond Features and Targets Correlation vs Causation Classification Framing Linear Regression I: Prediction and Residuals Prediction versus Causation Scores and Logits Loss and Fitting: Squared Error Potential Outcomes and Treatment Effects Sigmoid and Probabilities Logistic Regression Propensity Scores and Inverse Probability Weighting