Causal Inference Using Potential Outcomes

@article{Rubin2005CausalIU,
  title={Causal Inference Using Potential Outcomes},
  author={D. Rubin},
  journal={Journal of the American Statistical Association},
  year={2005},
  volume={100},
  pages={322 - 331}
}
  • D. Rubin
  • Published 2005
  • Mathematics
  • Journal of the American Statistical Association
Causal effects are defined as comparisons of potential outcomes under different treatments on a common set of units. Observed values of the potential outcomes are revealed by the assignment mechanism—a probabilistic model for the treatment each unit receives as a function of covariates and potential outcomes. Fisher made tremendous contributions to causal inference through his work on the design of randomized experiments, but the potential outcomes perspective applies to other complex… Expand
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References

SHOWING 1-10 OF 90 REFERENCES
Identification of Causal Effects Using Instrumental Variables
Identification of Causal Effects Using Instrumental Variables: Comment
Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches.
Principal stratification in causal inference.
Estimating causal effects of treatments in randomized and nonrandomized studies.
The central role of the propensity score in observational studies for causal effects
THE ESTIMATION OF CAUSAL EFFECTS FROM OBSERVATIONAL DATA
Compliance subsampling designs for comparative research: estimation and optimal planning.
Direct and Indirect Causal Effects via Potential Outcomes
...
1
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3
4
5
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