On Causes, Causal Inference, and Potential Outcomes
نویسندگان
چکیده
منابع مشابه
Causal Inference Using Potential Outcomes: Design, Modeling, Decisions
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 desig...
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Dear Editor, I read with interest the comment by Lindquist and Sobel (L&S) entitled: “Graphical models, potential outcomes and causal inference” (NeuroImage, 2010) in which they advocate the use of counterfactual language to explicate causal assumptions, and raise doubts on whether graphical models are generally useful for estimating causal effects. Their comment creates the impression, perhaps...
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ژورنال
عنوان ژورنال: International Journal of Epidemiology
سال: 2017
ISSN: 0300-5771,1464-3685
DOI: 10.1093/ije/dyw230