Bayesian approaches are becoming increasingly popular, but they can be overwhelming at first. This extensive guide will walk you through applications, libraries, and dependencies of causal discovery approaches.
The infinite possibilities of Bayesian techniques are also their weakness; The applications are huge and it can be problematic to understand how the techniques relate to different solutions and therefore different applications. In my previous blogs, I wrote about various topics such as structure learning, parameter learning, inference, 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 data sets? Can causal networks be determined without (without) response/treatment variables? How do you decide which search methods to use, such as PC, Hillclimbsearch, etc.? After reading this blog, you will know where to start and how to select the most appropriate Bayesian techniques for causal discovery for your use case. Take your time, take a…
