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. Potential outcomes define Causal Effects in all cases: randomized experiments and observational studies Break from the tradition before the 1970 s Key assumptions, such as stability (SUTVA) can be stated formally2. Model for the assignment mechanism must be explicated for all forms of Inference Assignment mechanism process for creating missing data in the potential outcomes Allows possible dependence of process on potential outcomes, , confounded designs Randomized experiments a special case whose benefits for Causal Inference can be formally stated3.

0-1.1 Basic Concepts of Statistical Inference for Causal Effects in Experiments and Observational Studies I. Framework 1. Basic Concepts: Units, treatments, and potential outcomes 2. Learning about causal effects: Replication, stability, and the assignment mechanism 3. Transition to statistical inference: The Perfect Doctor and Lord’s Paradox 4.

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