Unlocking the Power of Bayesian Approaches in Causal Discovery
Bayesian approaches have been gaining traction in the field of causal discovery, offering a powerful toolkit for unraveling complex relationships in data. However, diving into the world of Bayesian methods can be overwhelming initially, with a myriad of applications, libraries, and dependencies to navigate. Fear not, as this comprehensive guide is here to demystify the landscape of causal discovery approaches.
The versatility of Bayesian techniques presents both a world of opportunities and challenges. In previous blog posts, I have delved into topics like structure learning, parameter learning, inferences, and comparative analyses of Bayesian libraries. Building on this foundation, this blog will be your guide to navigating the diverse applications of Bayesian methods in causal discovery.
Through this article, you will learn how to construct causal networks, represented as Directed Acyclic Graphs, from both discrete and continuous datasets. You will explore the nuances of determining causal relationships with or without response variables, and discover the decision-making process behind selecting search methods like PC and Hillclimbsearch.
By the end of this blog, you will have a clear roadmap on where to begin your journey and how to choose the most suitable Bayesian techniques for your specific use case. Whether you are just starting out or looking to deepen your understanding, this guide will equip you with the knowledge and tools needed to harness the power of Bayesian approaches in causal discovery.