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Marc Bellemare's Metrics Mondays posts are pretty good: http://marcfbellemare.com/wordpress/metrics-mondays

Lots of practical tips for causal inference on real data.

Anything by Judea Pearl, but especially the Book of Why[1], is good. He comes at causality from a CS perspective, which I think would make sense for most people on here.

Economists also have a big causality literature which might be less accessible for HN folks but I think is still interesting and important. For a good intro to all that, I suggest Scott Cunningham’s “Causal Inference: The Mixtape.”[2]

[1] https://www.indiebound.org/book/9780465097609

[2] https://scunning.com/causalinference_norap.pdf

Pearls model based causality is from a set up the same as structural econometric models of the economists. Here, economists largely care about identification and partial identification (check work by Manski), which is at the same time related to causation but also a more practical matter of data and model choice.

Very interesting stuff all this

I find Frederick Eberhardt’s writing on causality to be particularly clear and instructive.