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Causal inference in genetic epidemiology: Mendelian randomization and beyond

Published on March 29, 2017   42 min

Other Talks in the Series: Statistical Genetics

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My name is Krista Fischer. And I'm working as a Senior Researcher at Estonian Genome Center, University of Tartu, Estonia. My background is in biostatistics and in genetic epidemiology. My research interest for about two decades has been causal inference, and in recent years, it has been more in the field of genetics, so Mendelian randomization is one of my favorite topics of research and also to teach.
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Today, we're going to talk about causal inference, causal effects, but causal effect may be simple in everyday language, but it's not so easy to define it mathematically. I introduced one possible way to talk about causal effects, so called language of counterfactual variables. Then I will briefly speak about causal graphs and show some examples related to confounding and adjustments because often we need to answer questions whether we should adjust for some confounders, potential confounders or not. And then I'll move to Mendelian randomization topics, specifically, instrumental variables estimation, so it can be used in more general cases in Mendelian randomization. And then we'll see what Mendelian randomization is. so it can The only possible way to analyze association structures with one genotype and two phenotypes, as you see there are actually more possible association structures and Mendelian randomization can be used only some assumptions are fulfilled. Then we will discuss these assumptions and ways to do checks, and we'll end with some references.

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Causal inference in genetic epidemiology: Mendelian randomization and beyond

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