The Complete Starters Guide For Causal Discovery using Bayesian Modeling.

The endless possibilities of Bayesian techniques are also its weakness; the applications are enormous, and it can be troublesome to understand how techniques are related to different solutions and thus applications. In my previous blogs, I have written about various topics such as structure learning, parameter learning, inferences, and a comparative overview of different Bayesian libraries. In this blog, I will walk you through the landscape of Bayesian applications, and describe how applications follow different causal discovery approaches. In other words, how do you create a causal network (Directed Acyclic Graph) using discrete or continuous datasets? Can you determine causal networks with(out) response/treatment variables? How do you decide which search methods to use such as PC, Hillclimbsearch, etc? After reading this blog you know where to start and the most appropriate techniques for your application. Take your time, grab a coffee, and enjoy the read.

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