Bayesian approaches have gotten more and more standard however could be overwhelming initially. This intensive information will stroll you thru purposes, libraries, and dependencies of causal discovery approaches.
The countless prospects of Bayesian methods are additionally their weak point; the purposes are huge, and it may be troublesome to know how methods are associated to totally different options and thus purposes. In my earlier blogs, I’ve written about varied subjects similar to construction studying, parameter studying, inferences, and a comparative overview of various Bayesian libraries. On this weblog publish, I’ll stroll you thru the panorama of Bayesian purposes, and describe how purposes comply with totally different causal discovery approaches. In different phrases, how do you create a causal community (Directed Acyclic Graph) utilizing discrete or steady datasets? Can you identify causal networks with(out) response/therapy variables? How do you resolve which search strategies to make use of similar to PC, Hillclimbsearch, and so on? After studying this weblog you’ll know the place to begin and choose essentially the most acceptable Bayesian methods for causal discovery on your use case. Take your time, seize a…