• Table of Contents
    • Abstract
    • Keywords
    • Article
      • Implications of the RCM for research design
      • Potential outcomes and causal effects
      • Brief history of potential outcomes to define causal effects
      • The assignment mechanism and assignment-based causal inference
      • Posterior predictive, or model-based, causal inference
      • Advantages of the RCM
    • See Also
    • Bibliography
    • How to cite this article

Rubin causal model

Guido W. Imbens and Donald B. Rubin
From The New Palgrave Dictionary of Economics, Second Edition, 2008
Edited by Steven N. Durlauf and Lawrence E. Blume
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The Rubin Causal Model (RCM), a framework for causal inference, has three distinctive features. First, it uses ‘potential outcomes’ to define causal effects at the unit level, first introduced by Neyman in the context of randomized experiments and randomization-based inference, but not used formally in non-randomized studies or with other modes of inference until Rubin (1974; 1975). Second is its formal use of a probabilistic assignment mechanism, which mathematically describes how treatments are given to units, with possible dependence on background variables and the potential outcomes themselves. Third is an optional probability distribution on all variables, including the potential outcomes, which thereby unifies frequentist and model-based forms of statistical inference for causal effects within one framework.
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How to cite this article

Imbens, Guido W. and Donald B. Rubin. "Rubin causal model." The New Palgrave Dictionary of Economics. Second Edition. Eds. Steven N. Durlauf and Lawrence E. Blume. Palgrave Macmillan, 2008. The New Palgrave Dictionary of Economics Online. Palgrave Macmillan. 16 January 2018 <http://www.dictionaryofeconomics.com/article?id=pde2008_R000247> doi:10.1057/9780230226203.1466

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