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Thanks, let me first say this all seems to be in support of my point that causality is not some obvious basic concept like it is usually treated. Apparently I n
by nonbel 10y ago
Thanks, let me first say this all seems to be in support of my point that causality is not some obvious basic concept like it is usually treated. Apparently I need to understand and accept other technical concepts such as "world model" and "surgery" before understanding "causal".
Second, it sounds like Pearl is in agreement that cause and effect do not appear to be physical aspects of the universe (ie something to be independently measured), but are rather wholly dependent on the person/device performing the measurements. By dependent I don't mean in the sense that pressing a ruler up a against an object slightly changes its length, or the uncertainty principle, etc. I mean that the perspective of the observer looks like it is central to Pearl's conception of causality. If this is the case, it should be possible to do science without any use of the cause/effect idea at all. However, perhaps it is a useful heuristic in many cases.
Third, I am always wary about arguments regarding "scientists do x". Many people are passing themselves off as scientists these days who are not actually doing science according to the usual definitions. For an example, just look at the dearth of even attempted replications or proposed theories making precise predictions in fields like health and social science. Unsurprisingly, the vast majority of what gets claimed in those areas seems to be wrong when it is checked. The widespread misinterpretation of and games surrounding p-values occurring in these fields of research goes without mention... My point here is just because some people claiming to be scientists behave a certain way doesn't mean that is the most (or even a) productive way to act.
- Mathnerd314 10y agoI think Pearl's arguments are primarily about science and humans, not about "scientists" as such. As a human, there is a very clear boundary distinction between the "in" and the "out". Furthermore, there seems be a strong tendency in human thinking to classify the world into objects and their causes or effects. So, for example, there's an acausal theory of physics but then the surrounding explanations are all narrative-based heuristics. And those narratives seem to be easier to process and manipulate, for humans. Also as Pearl also mentioned, this really only became a point of interest when computer science and artificial intelligence got going, because there non-narrative models seem to be successful as well, like neural networks, finite element methods, and so on. A similar perspective applies to p-hacking; it's not so much that people deviate from the "one true way" of doing statistics, as that there are a hundred conflicting myths of what statistics should be and often they pick the one that is most convenient, rather than searching through to find the most applicable.