Causal inference with Causallib
summary
Argues that prediction alone misleads—example: a clinical risk calculator shows predicted risk rising after treatment—and demonstrates concrete methods and a working tool: causallib is an open‑source Python package (scikit‑learn style API) that implements inverse probability weighting, outcome and composite estimators, survival models, and supports logistic regression or additive trees, cross‑validation, sample‑splitting and evaluation. Shows it runs, plugs into real pipelines, is on PyPI and GitHub, and narrows uncertainty when models and adjustments are chosen correctly.
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chapters (5)
- 0:00Causal inference intro
- 3:13Prediction limits in risk
- 7:51Experiments to observational data
- 15:58Estimating causal effects
- 23:38Summary
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