Transcription of Basic Concepts of Statistical Inference for Causal Effects ...
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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.
The following material is a summary of the course materials used in Quantitative Reasoning (QR) 33, taught by Donald B. Rubin at Harvard University. Prepared with assistance from Samantha Cook, Elizabeth Stuart, and Jim Greiner. c 2004, Donald B. Rubin Last update: 22 August 2005 1
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