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It’s interesting to study, but it doesn’t magically make hard problems tractable. It seems to make certain common mistakes less likely. When you do statistical

It’s interesting to study, but it doesn’t magically make hard problems tractable. It seems to make certain common mistakes less likely. When you do statistical analysis or machine learning to model p(x,y) or its friend p(y|x) it’s possible that you mistake correlations for causation (because there is no way to express directionality of dependency). If I have data about the measurement on a barometer and atmospheric pressure then I may learn a model that predicts higher pressure when the barometer reading is high. But if I manually force the barometer reading higher that doesn’t increase the atmospheric pressure. Causality provides a mathematical framework to express that idea. The problem I have with causality is how to find accurate causal diagrams from unstructured observational data. We _could_ guess and check every possible causal relationship, but that’s at least exponentially hard—in which case causality is useless. People seem to have reasonably good capabilities for generating candidate causal hypotheses from observational data (basically all of modern science), but most of the material I’ve found on causality focuses on the theoretical benefits it provides rather than on practical applications at scale. (I don’t care if we can automate finding the causal graph for barometer & air pressure; how do I find a causal graph for classifying fake/real news from plain text data?)

memexy 6y ago
I guess it's tricky because the real world is full of feedback loops. If you want a causal model for fake news then your model needs to include some representation of incentives for ad revenue and clickbait. How does the causal inference framework handle feedback loops?
- wenc 6y ago
  I believe DAG-based causal inference isn’t able to handle feedback loops (acyclic) or nonlinearity (linear). Nonlinearities include stuff like deadbands and delays. Control theory models handle these things just fine. But control models are hard to apply to sociological/epidemiological domains, where causal inference dominates. From what I gather, causal inference is useful for designing studies. I’m not sure if they’re used for prediction — would appreciate if someone in the know could chime in.
  - memexy 6y ago
    Interesting. I wonder if someone has tried to combine the two. I guess modern deep reinforcement learning is one such combination because it combines feedback (reinforcement) and probabilistic descriptions but maybe there are other interesting combinations of probability, causality, and feedback.
  - marmaduke 6y ago
    > DAG-based causal inference isn’t able to handle feedback loops (acyclic) or nonlinearity (linear) I don't think this should be true, and if it is, then "causal inference" should be qualified to refer only to a specific modeling framework. As a counter example, it's possible to formulate a nonlinear differential equation model of Covid spread and infer the parameters to construct a plausible, causal, generative model.

memexy 6y ago

I guess it's tricky because the real world is full of feedback loops. If you want a causal model for fake news then your model needs to include some representation of incentives for ad revenue and clickbait. How does the causal inference framework handle feedback loops?

wenc 6y ago
I believe DAG-based causal inference isn’t able to handle feedback loops (acyclic) or nonlinearity (linear). Nonlinearities include stuff like deadbands and delays. Control theory models handle these things just fine. But control models are hard to apply to sociological/epidemiological domains, where causal inference dominates. From what I gather, causal inference is useful for designing studies. I’m not sure if they’re used for prediction — would appreciate if someone in the know could chime in.
- memexy 6y ago
  Interesting. I wonder if someone has tried to combine the two. I guess modern deep reinforcement learning is one such combination because it combines feedback (reinforcement) and probabilistic descriptions but maybe there are other interesting combinations of probability, causality, and feedback.
- marmaduke 6y ago
  > DAG-based causal inference isn’t able to handle feedback loops (acyclic) or nonlinearity (linear) I don't think this should be true, and if it is, then "causal inference" should be qualified to refer only to a specific modeling framework. As a counter example, it's possible to formulate a nonlinear differential equation model of Covid spread and infer the parameters to construct a plausible, causal, generative model.