Hey!
I’ve been reading about causal inference recently and trying to incorporate those methods into my research as much as I can. My impression is that many others are trying to do the same in various fields. I thought to start a new topic to see if anyone here had more insight, tips, ideas, or advice on this in Julia. What does the workflow look like?
My favorite learning resource has been the Hernán & Robins book Causal Inference: What If. This book presents methods such as inverse probability weighting, parametric g-formula, g-estimation, doubly robust machine learning estimators, instrumental variable estimation, causal mediation analysis, target trial emulation, and of course DAGs and the backdoor criterion, but also SWIGs (single-world intervention graphs that extend DAGs to the counterfactual realm).
(I did just find CausalInference.jl and CausalELM.jl.)
