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But the whole argument in "casual calculus" is based on the assumption that you have a situation where controlled, randomized experiments are impossible. So why

by fnl 10y ago

But the whole argument in "casual calculus" is based on the assumption that you have a situation where controlled, randomized experiments are impossible. So why/how is that route not possible to design into recommender engines, churn prediction, or contextual bandits?

nabla9 10y ago
The discovery of causal relationships from purely observational data is called causal discovery. It's possible to detect the direction of causality with observation with some additional assumptions that are reasonable general. For example Additive Noise Models (ANM) assume that there is an additive noise structure in observational distribution. The key assumption is that if X causes Y, the noise in X can have an effect on Y but not vice versa. Additive noise model is near 80 per cent accurate in correctly determining cause-and-effect across large number of datasets. --- Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf; 17(32):1−102, 2016. http://jmlr.org/papers/v17/14-518.html http://jmlr.org/papers/v17/14-518.html Center for Causal Discovery web site http://www.ccd.pitt.edu/ http://www.ccd.pitt.edu/
- lngnmn 10y ago
  > The key assumption is that if X causes Y, the noise in X can have an effect on Y but not vice versa. Fine. What if you missed a hidden variable Z which also causes Y? What if there is also X1 which causes Y when no X is present. For example, a mapping of genetic mutations to actual diseases cannot be done from purely observational data without knowing the implementation - how particular proteins interact in this or that pipeline. Possible causal relationships does not establish or prove causality itself.
  - nabla9 10y ago
    >Fine. What if you missed a hidden variable Z which also causes Y? What if there is also X1 which causes Y when no X is present. Noise going trough Z -> Y is not present in X -> Y. Detecting X1 when X is not present is trivial. Y is not present and the noise from Y is not present. There is another cause besides Y. >Possible causal relationships does not establish or prove causality itself. Noise models can establish the arrow direction with very high probability. The strength of causality and other factors are considered separately
- fnl 10y ago
  I wasn't asking about that. Rather, I was referring to applying casual modeling (like recommending, A/B testing, churn prediction, etc) to cases where [I believe] using a randomized experiment to test the causality seems peculiar.

by fnl 10y ago

nabla9 10y ago

The discovery of causal relationships from purely observational data is called causal discovery. It's possible to detect the direction of causality with observation with some additional assumptions that are reasonable general. For example Additive Noise Models (ANM) assume that there is an additive noise structure in observational distribution. The key assumption is that if X causes Y, the noise in X can have an effect on Y but not vice versa. Additive noise model is near 80 per cent accurate in correctly determining cause-and-effect across large number of datasets. --- Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf; 17(32):1−102, 2016. http://jmlr.org/papers/v17/14-518.html http://jmlr.org/papers/v17/14-518.html Center for Causal Discovery web site http://www.ccd.pitt.edu/ http://www.ccd.pitt.edu/

lngnmn 10y ago
> The key assumption is that if X causes Y, the noise in X can have an effect on Y but not vice versa. Fine. What if you missed a hidden variable Z which also causes Y? What if there is also X1 which causes Y when no X is present. For example, a mapping of genetic mutations to actual diseases cannot be done from purely observational data without knowing the implementation - how particular proteins interact in this or that pipeline. Possible causal relationships does not establish or prove causality itself.
- nabla9 10y ago
  >Fine. What if you missed a hidden variable Z which also causes Y? What if there is also X1 which causes Y when no X is present. Noise going trough Z -> Y is not present in X -> Y. Detecting X1 when X is not present is trivial. Y is not present and the noise from Y is not present. There is another cause besides Y. >Possible causal relationships does not establish or prove causality itself. Noise models can establish the arrow direction with very high probability. The strength of causality and other factors are considered separately
fnl 10y ago
I wasn't asking about that. Rather, I was referring to applying casual modeling (like recommending, A/B testing, churn prediction, etc) to cases where [I believe] using a randomized experiment to test the causality seems peculiar.