milkos.bsky.social
I try to discover how we discover, to help others discover!
Master^3(Philosophy, Cognitive Science, Management & Leadership)
PhD candidate researching scientific practices with causal discovery algorithms
I love dancing:)
17 posts
213 followers
732 following
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True, the link between causal models and their target systems remains fuzzy in causality. Not sure we can grasp this framework from stochastic thermodynamics without mastering the framework foundations first. Getting into causality is though.
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Do you mean that if Y is caused by $X then difference between changing $ or X is meaningful only in dynamic systems ? Changing parameter is changing causal relationships on all phenomenon level, changing X is changing its state on units level. They are not same, even with same numerical effect.
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It is acceptable, but functional opacity has to be countered by external evaluation of the outcomes. For example competent judges should grade random 10% of the outcomes.
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Agree. I was not saying that it’s wrongly selling itself as something more.
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Where this quote come from?
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This is because causal framework is selling itself as something more then statistic, while basic argument is that it could provide experimental results without them. Nevertheless, there is a lot of work dealing with both experimental and observational data
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Now I get it. You mean HB paradigm as way of doing behavior economy. I was thinking about HB paradigm as program in cognitive science. You probably know the Anderson Rationality analysis regarding HB?
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But maybe I don’t understand what you mean by HB program, and why impossibility of finite set is so devastating to such program
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You say that HB will fall under its own weights because HB is not finite. However, notice that your argument could also support the claim: the HB changes on the go. So it’s not finite because it’s dynamical, and also the space of possible HB is changing.
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Moreover the test and train set seemed very similar, so the questions of test distribution and overfit may make the competition obsolete
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The logic of this comp is weird. They have the same method applied to all 25k test sets. It’s not a typical application, where we usually know smtg about the object of inquire and could choose algorithm that fit best to our problem. They measure best universal naive method, not real problem solving.
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Dude. But what about dynamic set of heuristics? We crate them and change them on the go.
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Meta (cognition) is intertwined self-monitoring and self-control. I think first-order science have meta-scientific elements: we are „just” specializing. I think that positive feedback loop can be very powerful for science advancement - yet hard to see, as within first order practice itself.
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Doing things that impact empirical research!
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Speaking as philosopher and social scientist and causal methodologists: clarifying what proper A and B is; criticizing the way to measure/intervene on them; picking/idealizing right phenomena; reassuring that it’s harder job then physics (I studied that too!); nudging them to have better theory.
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I think the @jakobrunge.bsky.social causal inference for time series is great. The very aim is to bridge methodological and domain specialist language. Also it build on Pearl Causal Hierarchy paper which is great intro to fundamental ideas. www.nature.com/articles/s43...
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Can I be added?