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- aggregation classification "A1".
- aggregation creator B676661.
- aggregation creator person.
- aggregation date "2012".
- aggregation format "application/pdf".
- aggregation hasFormat 3167740.bibtex.
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- aggregation isPartOf urn:issn:0340-6717.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Mathematics and Statistics".
- aggregation title "Causation and causal inference for genetic effects".
- aggregation abstract "Over the past three decades, substantial developments have been made on how to infer the causal effect of an exposure on an outcome, using data from observational studies, with the randomized experiment as the golden standard. These developments have reshaped the paradigm of how to build statistical models, how to adjust for confounding, how to assess direct effects, mediated effects and interactions, and even how to analyze data from randomized experiments. The congruence of random transmission of alleles during meiosis and the randomization in controlled experiments/trials, suggests that genetic studies may lend themselves naturally to a causal analysis. In this contribution, we will reflect on this and motivate, through illustrative examples, where insights from the causal inference literature may help to understand and correct for typical biases in genetic effect estimates.".
- aggregation authorList BK1039558.
- aggregation endPage "1676".
- aggregation issue "10".
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- aggregation volume "131".
- aggregation aggregates 3167840.
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