5 ms·
The Slippery Math of Causation
- kazinator 8y agoIf an catastrophic event occurs as a result of multiple necessary conditions, and one of those necessary conditions (even if not sufficient) is that some human was doing something they shouldn't have been doing, then of course it's that human's fault. Cause follows blame. We basically use "cause" as a synonym for "blame" in that situation. If there is no human there to blame do we then say that the cause is a coincidental combination of multiple spontaneous conditions, all necessary but not sufficient by themselves.
- garmaine 8y agoI don't think that holds up if you reduce to simple examples that are easily analyzed. If a billiard ball is pushed, it is the contact of the thing that pushed it which caused its motion, an equal and opposite reaction by Newton's 3rd law. If you did statistical analysis you'd discover that the chances of a ball moving depend very highly on whether the last ball to end up in a net was matching suit (colored or striped). But that's not a causal connection in the same sense. There's a causality in this level of physics that works perfectly well. It's not a coincidental combination of multiple spontaneous conditions that the ball moved -- it moved because it got pushed, and if it hadn't been pushed it wouldn't have moved.
- trhway 8y agothat seems to be the whole core of those discussions - statistics, as a science, operates only with correlations and has nothing to do with causation. As long as one stays inside the domain of statistics, one can never say whether something is causation or not. Causation is subject matter of other sciences which study specific machinery of various causations.
- AndrewKemendo 8y agoThe problem with Causation generally is that we choose arbitrary stopping points for "root causes," and fall into the Fallacy of the Single Cause. Pearl's Bayesian Statistics and Do-Calculus gives us a way to compute likelihoods around different observed events to give us some more insight into these mechanisms but from my reading so far, never give concrete solutions to when we should determine an action is not relevant.
- justinpombrio 8y ago> never give concrete solutions to when we should determine an action is not relevant Not relevant to what? What is the specific problem that Pearl does not resolve?
- cschmidt 8y agoSince the OP is prompted by Judea Pearl's new book, I'll ask here. There seem to be at least two schools of thought in causal statistics. The first is championed by Judea Pearl [1,2,3] and the other by Donald Rubin [4]. If I want to learn causal statistics, for use in ML, which school of thought would be more useful? I don't mean to prompt any causal flame wars, but it isn't obvious which approach is more useful. [1] https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X [2] https://www.amazon.com/Causality-Reasoning-Inference-Judea-Pearl/dp/052189560X [3] https://www.amazon.com/Causal-Inference-Statistics-Judea-Pearl/dp/1119186846 [4] https://www.amazon.com/Causal-Inference-Statistics-Biomedical-Sciences/dp/0521885884
- imh 8y agoStart with Rubin. Potential outcomes are more grounded and concretely interpretable. The epistemology is simple and won't get in your way, if you're inclined to dwell on those things. It also has the huge wealth of well developed techniques. Then move on to Pearl. (btw this is in no way a "standardized" answer, just my opinion about how to start with the basics)
- whatshisface 8y agoIf there is more than one "school of thought," then there cannot be any real difference[0] between their results: because if there was, it would count as an empirical resolution between them. Everything else is metaphysical paint and should be taken according to taste. [0] In saying this I'm assuming neither one is outright wrong.
- tlb 8y agoBut one way can be vastly easier to get results than another other way. Even easy but wrong ways can be useful, such as Newtonian mechanics. (It's computationally infeasible to predict, eg, robot movements with quantum mechanics.)
- whatshisface 8y agoNewtonian and quantum mechanics deliver different answers, which means that without approximation they are different theories - not different schools of thought. With approximation (in a certain limit), quantum and Newtonian mechanics are the same, and take the same amount of work to find answers - quantum mechanics yields Newtonian mechanics in that limit, doing "room temperature large scale" quantum mechanics means writing down Newton's laws. In this sense it's most accurate to say that Newtonian mechanics is a particular approximation to quantum mechanics that holds under certain circumstances. This other school-of-thought business (Bayesians vs. frequentists, pilot waves vs many worlds vs Copenhagen, everything like them) means choosing between different sets of words to describe the same thing. You are left with one of two cases: either they can be shown equivalent (in which case people are prone to keep on arguing over which one is better), or they are different (in which case one is wrong.)
- olavolav 8y agoShould any of you care for a more philosophical treatment of the idea of "cause", then I can wholeheartedly recommend: Bertrand Russel, On The Notion Of Cause Proceedings of the Aristotelian Society New Series, Vol. 13 (1912 - 1913), pp. 1-26 It's also surprisingly funny. https://www.jstor.org/stable/4543833 https://www.jstor.org/stable/4543833
- Karrot_Kream 8y agoThis is a form of philosophy specifically known as causal processes. [1] contains a good overview of this that presents Russel and critics (Salmon et. al). (I prefer that philosophy be taken in context rather than from the mouth of a particular philosopher). 1: https://plato.stanford.edu/entries/causation-process/ https://plato.stanford.edu/entries/causation-process/
- leephillips 8y agoHere is a copy of the Russel paper free for all: https://users.drew.edu/jlenz/notion-of-cause/br-notion-of-cause.html https://users.drew.edu/jlenz/notion-of-cause/br-notion-of-ca... Thanks for calling attention to this - it is interesting (and funny).
- macawfish 8y agoJust wanted to throw out a related Quanta article about Sugihara's method: https://www.quantamagazine.org/chaos-theory-in-ecology-predicts-future-populations-20151013/ https://www.quantamagazine.org/chaos-theory-in-ecology-predi... Sugihara's method is a way of quantifying causal relationships in nonlinear systems with attractors that would be cumbersome to model (hence the "equation free" description).
- whitten 8y agoSo has anyone looked into the issues involved in explaining this level of causation to any AI systems? It seems this might be work, but interesting.