Causal Inference with Bayesian Networks
Build Bayesian Networks and Causal Inference Models with R and Python
RBCreated by Yousri El Fattah, Reza Bagheri
Gain a practical understanding of Bayesian networks and causal inference to analyze real-world data. Learn how to use graphical models for probabilistic reasoning and estimate treatment effects with hands-on examples in R and Python. Develop skills to design and implement causal analysis workflows for evidence-based decision-making.
Packt | May 2026 | 686 min
What You Will Learn
You will start by building a foundation in Bayesian networks and graphical models, then move on to causal inference techniques using real datasets. Through step-by-step examples and coding exercises in R and Python, you will practice applying probabilistic reasoning, intervention analysis, and treatment effect estimation to solve practical problems.
Key Features
- Design Bayesian networks for knowledge representation and inference tasks
- Estimate causal effects from observational data using machine learning methods
- Build and implement causal modeling workflows in both R and Python
Target Audience
Ideal for data scientists, analysts, and technical professionals with some experience in data analysis or programming. If you want to deepen your understanding of causal inference, apply machine learning to estimate treatment effects, or build decision-support tools using R and Python, you will find practical value and actionable skills here.
