Pearl's causal model is a decent didactic tool, but it seems to have little relevance when it comes to trying to figure out real-world problems. (If any actual

Pearl's causal model is a decent didactic tool, but it seems to have little relevance when it comes to trying to figure out real-world problems. (If any actual scientific breakthroughs have come from Pearl's approach, I'd love to hear about them. I don't think there are any.) I believe the disagreement between Gelman and Pearl may come down to the fact that Gelman often deals with actual estimation problems where you have to find causal parameters using scanty and imperfect data, with no strong theory to guide decisions, while Pearl is focused on toy models where all the limitations and uncertainties of real problems are assumed away. Donald Rubin has said, surely correctly, that design trumps analysis in causal inference. Pearl's approach seems to be the opposite--all focus is on the analytical details. For practicing scientists, I think this article this article provides a much more useful model for causal inference: https://academic.oup.com/ije/article/45/6/1787/2617188 https://academic.oup.com/ije/article/45/6/1787/2617188 See Textbox 3 where different approaches to studying the relation between smoking and low birthweight are described. The various approaches rely on different assumptions and any one study design may not be convincing by itself, but the way their results converge ("triangulation") is very convincing. AFAIK, none of the studies used DAGs, yet the causal evidence provided is stronger than any DAG could provide.