The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Rubin Causal Model

  • Guido W. Imbens
  • Donald B. Rubin
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2469

Abstract

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.

Keywords

Assignment mechanism Assignment-based inference Bayesian inference Causal inference Fisher, R. A. Haavelmo, T. Hurwicz, L. Instrumental variables Interval estimates Markov chain Monte Carlo methods Matching Multiple imputation Neyman, J. Posterior predictive distribution Potential outcomes Principal stratification Probability Program evaluation Propensity scores Randomization-based inference Randomized experiment Regression coefficients Roy model Rubin causal model Simultaneous equations models Tinbergen, J. treatments Units 
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© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Guido W. Imbens
    • 1
  • Donald B. Rubin
    • 1
  1. 1.