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0:00
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.
0:31
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.
