Basic Concepts of Statistical Inference for Causal Effects ...

Basic Concepts of Statistical Inferencefor Causal Effects in Experimentsand Observational StudiesDonald B. RubinDepartment of StatisticsHarvard UniversityThe following material is a summary of the course materials used in Quantitative Reasoning (QR) 33, taught byDonald B. Rubin at Harvard University. Prepared with assistance fromSamantha Cook, Elizabeth Stuart, and 2004, Donald B. RubinLast update: 22 August perspective on Causal Inference taken in this course is often referred to as the Rubin Causal Model ( ,Holland, 1986) to distinguish it from other commonly used perspectives such as those based on regression or relativerisk models. Three primary features distinguish the Rubin Causal Model:1.

III. Causal inference based on predictive distributions of potential outcomes 12. Predictive inference – intuition under ignorability 13. Matching to impute missing potential outcomes – donor pools 14. Fitting distinct predictive models within each treatment group 15. Formal predictive inference – Bayesian 16.

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  Inference, Matching, Casual, Causal inference, Inference for causal

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