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Rubin causal model, the Glossary

Index Rubin causal model

The Rubin causal model (RCM), also known as the Neyman–Rubin causal model, is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes, named after Donald Rubin.[1]

Table of Contents

  1. 22 relations: Annals of Statistics, Average treatment effect, Blood pressure, Cambridge University Press, Causal inference, Causality, Conceptual framework, Counterfactual conditional, Donald Rubin, Dorota Dabrowska, Guido Imbens, Instrumental variables estimation, Jerzy Neyman, Journal of Educational and Behavioral Statistics, Journal of Educational Psychology, Journal of the American Statistical Association, Paul W. Holland, Principal stratification, Propensity score matching, Statistical inference, Structural equation modeling, Variance.

  2. Causal inference
  3. Econometric models
  4. Experiments
  5. Observational study
  6. Statistical models

Annals of Statistics

The Annals of Statistics is a peer-reviewed statistics journal published by the Institute of Mathematical Statistics.

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Average treatment effect

The average treatment effect (ATE) is a measure used to compare treatments (or interventions) in randomized experiments, evaluation of policy interventions, and medical trials. Rubin causal model and average treatment effect are experiments.

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Blood pressure

Blood pressure (BP) is the pressure of circulating blood against the walls of blood vessels.

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Cambridge University Press

Cambridge University Press is the university press of the University of Cambridge.

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Causal inference

Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system.

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Causality

Causality is an influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause.

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Conceptual framework

A conceptual framework is an analytical tool with several variations and contexts.

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Counterfactual conditional

Counterfactual conditionals (also contrafactual, subjunctive or X-marked) are conditional sentences which discuss what would have been true under different circumstances, e.g. "If Peter believed in ghosts, he would be afraid to be here." Counterfactuals are contrasted with indicatives, which are generally restricted to discussing open possibilities.

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Donald Rubin

Donald Bruce Rubin (born December 22, 1943) is an Emeritus Professor of Statistics at Harvard University, where he chaired the department of Statistics for 13 years.

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Dorota Dabrowska

Dorota Maria Dabrowska is a Polish statistician known for applying nonparametric statistics and semiparametric models to counting processes and survival analysis.

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Guido Imbens

Guido Wilhelmus Imbens (born 3 September 1963) is a Dutch-American economist whose research concerns econometrics and statistics.

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Instrumental variables estimation

In statistics, econometrics, epidemiology and related disciplines, the method of instrumental variables (IV) is used to estimate causal relationships when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment.

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Jerzy Neyman

Jerzy Neyman (April 16, 1894 – August 5, 1981; born Jerzy Spława-Neyman) was a Polish mathematician and statistician who first introduced the modern concept of a confidence interval into statistical hypothesis testing and revised Ronald Fisher's null hypothesis testing with Egon Pearson.

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Journal of Educational and Behavioral Statistics

The Journal of Educational and Behavioral Statistics is a peer-reviewed academic journal published by SAGE Publications on behalf of the American Educational Research Association and American Statistical Association.

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Journal of Educational Psychology

The Journal of Educational Psychology is a peer-reviewed academic journal that was established in 1910 and covers educational psychology.

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Journal of the American Statistical Association

The Journal of the American Statistical Association (JASA) is the primary journal published by the American Statistical Association, the main professional body for statisticians in the United States.

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Paul W. Holland

Paul William Holland (born 25 April 1940) is an American statistician.

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Principal stratification

Principal stratification is a statistical technique used in causal inference when adjusting results for post-treatment covariates. Rubin causal model and Principal stratification are causal inference.

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Propensity score matching

In the statistical analysis of observational data, propensity score matching (PSM) is a statistical matching technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. Rubin causal model and propensity score matching are causal inference and observational study.

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Statistical inference

Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability.

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Structural equation modeling

Structural equation modeling (SEM) is a diverse set of methods used by scientists doing both observational and experimental research.

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Variance

In probability theory and statistics, variance is the expected value of the squared deviation from the mean of a random variable.

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See also

Causal inference

Econometric models

Experiments

Observational study

Statistical models

References

[1] https://en.wikipedia.org/wiki/Rubin_causal_model

Also known as Rubin Causal Inference Model, SUTVA.