Learning Bayesian Statistics
Learning Bayesian Statistics
Alexandre Andorra
#141 AI Assisted Causal Inference, with Sam Witty
1 hour 37 minutes Posted Sep 18, 2025 at 11:00 am.
Bridging Mechanistic and Data-Driven Models09:13 Understanding Causal Probabilistic Programming12:10 ChiRho and Its Design Principles15:03 ChiRho’s Functionality and Use Cases17:55 Counterfactual Worlds and Mediation Analysis20:47 Efficient Estimation in ChiRho24:08 Future Directions for Causal AI50:21 Understanding the Do-Operator in Causal Inference56:45 ChiRho’s Role in Causal Inference and Bayesian Modeling01:01:36 Roadmap and Future Developments for ChiRho01:05:29 Real-World Applications of Causal Probabilistic Programming01:10:51 Challenges in Causal Inference Adoption01:11:50 The Importance of Causal Claims in Research01:18:11 Bayesian Approaches to Causal Inference01:22:08 Combining Gaussian Processes with Causal Inference01:28:27 Future Directions in Probabilistic Programming and Causal InferenceThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad...
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Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Get early access to Alex's next live-cohort courses!Enroll in the Causal AI workshop, to learn live with Alex (15% off if you're a Patron of the show)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Causal inference is crucial for understanding the impact of interventions in various fields.ChiRho is a causal probabilistic programming language that bridges mechanistic and data-driven models.ChiRho allows for easy manipulation of causal models and counterfactual reasoning.The design of ChiRho emphasizes modularity and extensibility for diverse applications.Causal inference requires careful consideration of assumptions and model structures.Real-world applications of causal inference can lead to significant insights in science and engineering.Collaboration and communication are key in translating causal questions into actionable models.The future of causal inference lies in integrating probabilistic programming with scientific discovery.Chapters:05:53 Bridging Mechanistic and Data-Driven Models09:13 Understanding Causal Probabilistic Programming12:10 ChiRho and Its Design Principles15:03 ChiRho’s Functionality and Use Cases17:55 Counterfactual Worlds and Mediation Analysis20:47 Efficient Estimation in ChiRho24:08 Future Directions for Causal AI50:21 Understanding the Do-Operator in Causal Inference56:45 ChiRho’s Role in Causal Inference and Bayesian Modeling01:01:36 Roadmap and Future Developments for ChiRho01:05:29 Real-World Applications of Causal Probabilistic Programming01:10:51 Challenges in Causal Inference Adoption01:11:50 The Importance of Causal Claims in Research01:18:11 Bayesian Approaches to Causal Inference01:22:08 Combining Gaussian Processes with Causal Inference01:28:27 Future Directions in Probabilistic Programming and Causal InferenceThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad...