4 ms·
Want to make good business decisions? Learn causality
- lonelappde 7y agoNote that the article subtly admits that you can subvert the objectivity in your model-based decisions by making your subjecive choices of how to build the graph.
- sjg007 7y agoI think the graph itself is useful for that exact reason. It lets us see the the specific model assumptions and generate the testable conditions.
- smu3l 7y agoThis is always true: you're always making some strong assumptions when trying to make a causal claim. Using a causal framework, DAGs in this case, at least makes those assumptions explicit.
- runningmike 7y agoNice article. But playing and learning, thinking and debating about causal loops is Better imho. So I created a loopy clone: https://nocomplexity.com/causalloopdiagram/ https://nocomplexity.com/causalloopdiagram/
- Anon84 7y agoFor those of you interest in learning more about causality, I just announced on my mailing list (https://data4sci.com/latest https://data4sci.com/latest) that I'm working on a series of blog posts covering the contents of Judea Pearls's Causal Inference in Statistics - A Primer (https://amzn.to/39G5lWl https://amzn.to/39G5lWl, affiliate link) using Python.
- coldtea 7y agoHere's a relevant video for causality: https://www.youtube.com/watch?v=1dWjKkF0Zi4 https://www.youtube.com/watch?v=1dWjKkF0Zi4
- dhimes 7y agoWell, there goes the afternoon.
- peterjussi 7y agoCausality.
- h3ctic 7y agoSuper interesting article. I can recommend Pearl's Book of Why to learn more about it Although to me it's still quite difficult to apply the theoretical knowledge to day-to-day applications. Any references or ideas?
- Anon84 7y agoMaybe this will help? https://news.ycombinator.com/item?id=21994976 https://news.ycombinator.com/item?id=21994976
- satvikpendem 7y agoI find that as well. I've also read Book of Why but it's difficult to know when and how to use them. Perhaps it's just more practice of writing causal diagrams, as Pearl seems to do with ease, just due to the years of practice he's had. Something like programming, I guess.
- briefcomment 7y agoThis is common sense. Nothing more than you would learn in an introduction to regression analysis class. Still helpful to those not familiar with it. *edit: To those downvoting, is there anything especially insightful that I missed?
- throw_14JAS 7y agoI didn't downvote, but your comment came across as dismissive. Personally, I think there's a world of difference between the aphorism of "correlation does not equal causation" and actually understanding how causation works. I understand the former quite well, but haven't much a clue about the latter. (And I'm not alone!)
- briefcomment 7y agoGot it. I left my comment for the benefit of those who have learned regression. I read the article expecting something new, seeing how highly upvoted the post was on HN, but realized it was basic information. If they removed the symbols from the article, I bet the article would be accessible to way more people. Regarding causation, I think the essential points are: - there is never any true causation (e.g. Even though it is always dark when I close my eyelids, I can never be sure that closing my eyelids causes the darkness. How can I know that the next time I close my eyelids, it won't be something other than dark?) - you can see if a correlation may have a causal relationship by applying common sense to the chronology of events (if darkness correlates highly with closed eyelids, and if I notice that, chronologically, darkness has always followed closing my eyelids, I can be more confident that the relationship may be causal. Stats help formalize this process.)
- smu3l 7y agoChronology isn't enough though. Roosters crow before dawn.
- briefcomment 7y agoCorrect. That's why I mentioned common sense.