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

Someone posted this flowchart in a previous HN thread on Causal Inference frameworks: https://www.bradyneal.com/which-causal-inference-book (I picked up Counterfactuals and Causal Inference and Elements of Causal Inference, and would recommend both).

My belief is mostly intuitive and likely more extreme than most, and I don't know of a good single source to point to. John Ioannidis' writings are probably a good starting point: http://retractionwatch.com/2016/09/13/we-have-an-epidemic-of.... Searching for "convenience sampling" on Andrew Gelman's blog yields lots of good discussion in the comments: http://andrewgelman.com/?s=%22convenience+sample%22. Miguel Hernan's book on Causal Inference gives a good sense of the pitfalls of biased sampling: https://www.hsph.harvard.edu/miguel-hernan/causal-inference-.... Sorry I can't do better, and maybe others can add better sources.

P.S. I enjoyed the way you formatted and responded to your parent comment.

Thanks, the style is sometimes referred to as "interleaved" or "inline", as opposed to "top posting" and "bottom posting": https://brooksreview.net/2011/01/interleaved-email/. It was the norm for early online communications, but has mostly fallen out of favor. I think it works very well for some situations, although it too is surprisingly controversial: https://news.ycombinator.com/item?id=5233428.

I recently read Pearl's recent "The Book of Why", and thought it was excellent: https://www.basicbooks.com/titles/judea-pearl/the-book-of-wh...

Unlike his previous books this is intended for general audiences rather than practitioners. It offers a good overview of Causal Inference, as well as a personal take on why there is such a split between his graphical approach and others such as SEM.

Overall, Pearl is unabashedly optimistic that statistics is finally on the verge of a "causal revolution", and this book tries to describe what that means. I'd recommend it highly, either as standalone or as background to accompany his more technical works.

For anyone wanting to learn causal inference (the first item in the list), I highly recommend "Causal Inference, the Mixtape" by Scott Cunningham, a professor of economics at Baylor University. Scott has been writing this book incrementally in the open for the last couple of years, and recently completed and published it, and it is a thorough introduction to numerous techniques for inferring causation in different contexts. https://www.scunning.com/mixtape.html