Causal Inference with Bayesian Networks in Python

Unlock deeper insights from your data by understanding cause-and-effect relationships, not just correlations. This course provides a foundational understanding of Bayesian Networks and equips you with the Python skills to build, analyze, and interpret these powerful probabilistic graphical models for causal inference. What you'll learn: * Understand the core principles of probabilistic graphical models and conditional independence. * Learn to construct and represent causal relationships using Bayesian Networks. * Apply Bayes' Theorem for probabilistic inference and belief updating within networks. * Build and analyze basic Bayesian Network models using Python. * Identify and interpret key causal graph structures, including common cause and common effect. * Practice evaluating causal hypotheses and making data-driven predictions. * Explore fundamental concepts of modern probabilistic programming for model implementation. The course begins with foundational probability concepts and graph theory, progressing to the construction and analysis of Bayesian Networks, culminating in practical Python application and interpretation. This course is ideal for beginners in data science, analytics, or machine learning who want to understand causal inference. No prior experience with Bayesian Networks is required, though a basic familiarity with Python programming is beneficial. Begin your journey into causal modeling and unlock new analytical capabilities.