Bayesian approaches have gotten more and more well-liked however might be overwhelming at first. This intensive information will stroll you thru functions, libraries, and dependencies of causal discovery approaches.
The limitless prospects of Bayesian methods are additionally their weak point; the functions are monumental, and it may be troublesome to grasp how methods are associated to completely different options and thus functions. In my earlier blogs, I’ve written about numerous matters similar to construction studying, parameter studying, inferences, and a comparative overview of various Bayesian libraries. On this weblog submit, I’ll stroll you thru the panorama of Bayesian functions, and describe how functions observe completely different causal discovery approaches. In different phrases, how do you create a causal community (Directed Acyclic Graph) utilizing discrete or steady datasets? Can you establish causal networks with(out) response/remedy 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 start out and learn how to choose probably the most acceptable Bayesian methods for causal discovery to your use case. Take your time, seize a…
