Examples of solid causal inferences from purely observational data


I would like to catalog here a few great teaching examples where modern principles of causal inference are used to make solid causality statements from purely observational data. Contributions with brief background, reasoning, and results are welcome. Methods used would include DAGs, methods of Judea Pearl, Miquel Hernán, Ellie Murray, etc., the use of instrumental variables with exceptionally well-supported instruments that are not randomization, and would need to include answers to the original causal question.

Examples that do not qualify for inclusion:

  • A smart analysis of observational data that mimics an RCT by having strict inclusion criteria, limited missing data, intention to treat, etc., but does not use causal inference methods per se.


What is considered modern causal inference? Hernan type methods. You aren’t looking for things like smoking, thalidomide, Zika virus, hypertension (CVD risk factors), etc?



Will clarify on original post.

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Most those papers seem to be retrospective (benefit of knowing true causal effect from other method) or too theoretical for application purposes.



Here’s an example using Instrumental Variables to examine the effects of early vs late critical care admission for deteriorating ward patients: https://link.springer.com/article/10.1007/s00134-018-5148-2

Here’s the accompanying editorial: https://link.springer.com/article/10.1007/s00134-018-5194-9



At the end of my Bayesian class, I teach causal inference examples with observational data from “Mixing Methods: A Bayesian Approach” by Macartan Humphreys and Alan Jacobs, which (I believe) was the first paper using Stan published in the American Political Science Review. Here is a Google scholar link but there is an ungated version along with code and a video on Humphreys’ webpage.

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