https://www.bris.ac.uk/integrative-epidemiology/intranet/training/
In an epidemiological context, whether stated explicitly or not, we typically are referring to a counterfactual condition. A counterfactual condition is where we suppose that the outcome would not have happened in an alternate reality where all things were identical except for the exposure.
For example, if an individual had body mass index = 23 instead of body mass index = 27, but all else was identical, what would the impact be on their bone mineral density?
This is clearly a theoretical question that cannot be answered in real life. In order to approximate answers to this question, epidemiologists rephrase the question as "what is the causal effect of body mass index on bone mineral density?"
Below is a non-exhaustive list of a number of experimental designs and statistical methods that are widely used for causal inference. For some background reading on causality in epidemiologists I recommend the following paper:
The following youtube videos provide a very gentle introduction to the statistics that underlie instrumental variable analysis
Instrumental Variables - an introduction
Instrumental Variables intuition - part 1
Instrumental Variables intuition - part 2
Instrumental Variables example - returns to schooling
Two Stage Least Squares - an introduction
Two Stage Least Squares - example
- A two minute primer on mendelian randomisation
- George Davey Smith - How our genes conduct radomised trials
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Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey Smith G. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Statistics in Medicine 2008; 27: 1133-63.
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Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International Journal of Epidemiology 2015; 512–525. doi: 10.1093/ije/dyv080.
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Lawlor DA. Two-sample Mendelian randomization: opportunities and challenges. International Journal of Epidemiology 2016; 908–915. doi: 10.1093/ije/dyw127.
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Haycock PC, Burgess S, Wade KH, Bowden J, Relton C, Davey Smith. Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies. American Journal of Clinical Nutrition 2016; 103: 965–78.