"Draw your assumptions before your conclusions."

5 years ago we launched the first version of the #CausalDiagrams course via HarvardX and edX.

This was the official trailer:
https://www.youtube.com/watch?v=SB2FxG-SdEQ

Since then, about 80,000 people in 180 countries have registered. The course is free for everyone in the world.

If you are interested in learning about Directed Acyclic Graphs (DAGs) and Single-World Intervention Graphs (SWIGs) for #causalinference, check it out:
https://www.edx.org/learn/data-analysis/harvard-university-causal-diagrams-draw-your-assumptions-before-your-conclusions

Remarks about #m-bias; bigger picture: why #longitudinal data are generally needed for #causalinference ( #causaldiagrams are not enough)

https://go-bayes.github.io/b-causal/posts/m-bias/m-bias.html

My #introduction:

I repurpose observational #RealWorldData into scientific evidence for the prevention and treatment of human disease. At #CAUSALab, we often do so by explicitly emulating a #TargetTrial. Other times we analyze #RandomizedTrials.

I teach #causalinference methods at the #Harvard T.H. Chan School of #PublicHealth. My online course #CausalDiagrams and “Causal Inference" #WhatIfBook (with James Robins) are free. See my profile.

#epidemiology #epiverse
#statistics
#datascience
#AI