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
JEL classifications
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Imbens, G.W., Rubin, D.B. (2018). Rubin Causal Model. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2469
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DOI: https://doi.org/10.1057/978-1-349-95189-5_2469
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