Cause, effect, and observational studies

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Distinguishing cause from effect using observational data: methods and benchmarks Distinguishing cause from effect using observational data: methods and benchmarks

The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X,Y . This was often considered to be impossible. Nevertheless, several approaches for addressing this bivariate causal discov...

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Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks

The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X,Y . An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variab...

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Effect Modification and Design Sensitivity in Observational Studies

in Observational Studies Jesse Y. Hsu1, Dylan S. Small, Paul R. Rosenbaum University of Pennsylvania, Philadelphia Summary. In an observational study of treatment effects, subjects are not randomly assigned to treatment or control, so differing outcomes in treated and control groups may reflect a bias from nonrandom assignment rather than a treatment effect. After adjusting for measured pretrea...

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Low-Carbohydrate Diets and All-Cause Mortality: A Systematic Review and Meta-Analysis of Observational Studies

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ژورنال

عنوان ژورنال: The Journal of Wildlife Management

سال: 2013

ISSN: 0022-541X

DOI: 10.1002/jwmg.512