Applications of randomness | Causal inference

Mendelian randomization

In epidemiology, Mendelian randomization (commonly abbreviated to MR) is a method using measured variation in genes to interrogate the causal effect of an exposure on an outcome. Under key assumptions (see below), the design reduces both reverse causation and confounding, which often substantially impede or mislead the interpretation of results from epidemiological studies. The study design was first proposed in 1986 and subsequently described by Gray and Wheatley as a method for obtaining unbiased estimates of the effects of a putative causal variable without conducting a traditional randomized controlled trial (i.e. the "gold standard" in epidemiology for establishing causality). These authors also coined the term Mendelian randomization. (Wikipedia).

Mendelian randomization
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(PP 6.1) Multivariate Gaussian - definition

Introduction to the multivariate Gaussian (or multivariate Normal) distribution.

From playlist Probability Theory

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Discrete Math - 7.2.2 Random Variables and the Binomial Distribution

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From playlist Discrete Math I (Entire Course)

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What is the binomial distribution and what are binomial random variables? In today’s video probability theory lesson, we give a thorough introduction to the binomial distribution and binomial random variables. We’ll discuss its expected value and variance, and we will go over the probabili

From playlist Probability Theory

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(PP 6.3) Gaussian coordinates does not imply (multivariate) Gaussian

An example illustrating the fact that a vector of Gaussian random variables is not necessarily (multivariate) Gaussian.

From playlist Probability Theory

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(PP 5.1) Multiple discrete random variables

(0:00) Definition of a random vector. (1:50) Definition of a discrete random vector. (2:28) Definition of the joint PMF of a discrete random vector. (7:00) Functions of random vectors. A playlist of the Probability Primer series is available here: http://www.youtube.com/view_play_list?p=

From playlist Probability Theory

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Statistics: Sampling Methods

This lesson introduces the different sample methods when conducting a poll or survey. Site: http://mathispower4u.com

From playlist Introduction to Statistics

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029 - Mendelian Genetics Paul Andersen explains simple Mendelian genetics. He begins with a brief introduction of Gregor Mendel and his laws of segregation and independent assortment. He then presents a number of simple genetics problems along with their answers. He also explains how a

From playlist AP Biology Video Essentials

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Amandine Véber : The infinitesimal model of phenotypic evolution - a microscopic approach

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From playlist Probability and Statistics

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Alison Etheridge & Nick Barton: Applying the infinitesimal model

The infinitesimal model is based on the assumption that, conditional on the pedigree, the joint distribution of trait values is multivariate normal, then, selecting parents does not alter the variance amongst offspring. We explain how the infinitesimal model extends to include dominance as

From playlist Probability and Statistics

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From playlist 10 - Biology

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From playlist Second Bangalore School on Population Genetics and Evolution

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From playlist Evolution, Ecology and Behavior with Stephen C. Stearns

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GWAS in structured populations by Magnus Nordborg

The Third Bangalore School on Population Genetics and Evolution DATE:05 March 2018 to 17 March 2018 VENUE:Ramanujan Lecture Hall, ICTS Bangalore. No living organism escapes evolutionary change. Evolutionary biology thus connects all biological disciplines. To understand the processes dri

From playlist Third Bangalore School on Population Genetics and Evolution

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The Most Powerful Tool Based Entirely On Randomness

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From playlist Classical Physics by Parth G

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Random and systematic error explained: from fizzics.org

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From playlist Units of measurement

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Bayesian Networks 1 - Overview | Stanford CS221: AI (Autumn 2021)

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From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2021

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Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.

From playlist Learning medical statistics with python and Jupyter notebooks

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SciShow Quiz Show: The Ultimate Tiebreaker

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Confounding | Instrumental variables estimation | Path analysis (statistics) | Correlation does not imply causation | Randomized controlled trial