[LINK] If correlation doesn’t imply causation, then what does?

Strilanc

A post about how, for some causal models, causal relationships can be inferred without doing experiments that control one of the random variables.

If correlation doesn’t imply causation, then what does?

To help address problems like the two example problems just discussed, Pearl introduced a causal calculus. In the remainder of this post, I will explain the rules of the causal calculus, and use them to analyse the smoking-cancer connection. We’ll see that even without doing a randomized controlled experiment it’s possible (with the aid of some reasonable assumptions) to infer what the outcome of a randomized controlled experiment would have been, using only relatively easily accessible experimental data, data that doesn’t require experimental intervention to force people to smoke or not, but which can be obtained from purely observational studies.

A post about how, for some causal models, causal relationships can be inferred without doing experiments that control one of the random variables.

If correlation doesn’t imply causation, then what does?

To help address problems like the two example problems just discussed, Pearl introduced a causal calculus. In the remainder of this post, I will explain the rules of the causal calculus, and use them to analyse the smoking-cancer connection. We’ll see that even without doing a randomized controlled experiment it’s possible (with the aid of some reasonable assumptions) to infer what the outcome of a randomized controlled experiment would have been, using only relatively easily accessible experimental data, data that doesn’t require experimental intervention to force people to smoke or not, but which can be obtained from purely observational studies.

Do you know if there's an efficient algorithm for determining when two subsets of a DAG are d-separated given another? The naive algorithm seems to be a bit slow.

http://www.gatsby.ucl.ac.uk/~zoubin/course05/BayesBall.pdf

Amusing name, linear time algorithm. Also amusingly I happen to have direct line of sight on the author while writing this post :).

In some sense, we know a priori that d-separation has to be linear time because it is a slightly fancy graph traversal. If you don't like Bayes Ball, you can use the moralization algorithm due to Lauritzen (described here:

http://www.stats.ox.ac.uk/~steffen/teaching/grad/graphicalmodels.pdf

see slide titled "alternative equivalent separation"), which is slight... (read more)