An Extensive Starter Guide For Causal Discovery Using Bayesian Modeling

Bayesian approaches are becoming increasingly popular but can be overwhelming at the start. This extensive guide will walk you through applications, libraries, and dependencies of causal discovery approaches.

Landscape of Unsupervised Causal Discovery. Image by the author.

The endless possibilities of Bayesian techniques are also their 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 post, 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’ll know where to start and how to select the most appropriate Bayesian techniques for causal discovery for your use case. Take your time, grab a…