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.

based) methods and predictive (model-based or Bayesian) methods of causal inference • One unified perspective for distinct methods of causal inference instea d of two separate perspectives, one traditionally used for randomized experiment, the other traditionally used for observational studies

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  Methods, Studies, Observational, Bayesian, Observational studies

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