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> 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
by 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