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.