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• Get early access to Alex's next live-cohort courses!
• Intro to Bayes Course (first 2 lessons free)
• Advanced Regression Course (first 2 lessons free)

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:

• BART as a core tool: Gabriel explains how Bayesian Additive Regression Trees provide robust uncertainty quantification and serve as a reliable baseline model in many domains.
• Rust for performance: His Rust re-implementation of BART dramatically improves speed and scalability, making it feasible for larger datasets and real-world IoT applications.
• Strengths and trade-offs: BART avoids overfitting and handles missing data gracefully, though it is slower than other tree-based approaches.
• Big data meets Bayes: Gabriel shares strategies for applying Bayesian methods with big data, including when variational inference helps balance scale with rigor.
• Optimization and decision-making: He highlights how BART models can be embedded into optimization frameworks, opening doors for sequential decision-making.
• Open source matters: Gabriel emphasizes the importance of communities like PyMC and Bambi, encouraging newcomers to start with small contributions.

Chapters:

05:10 – From economics to IoT and Bayesian statistics

18:55 – Introduction to BART (Bayesian Additive Regression Trees)

24:40 – Re-implementing BART in Rust for speed and scalability

32:05 – Comparing BART with Gaussian Processes and other tree methods

39:50 – Strengths and limitations of BART

47:15 – Handling missing data and different likelihoods

54:30 – Variational inference and big data challenges

01:01:10 – Embedding BART into optimization and decision-making frameworks

01:08:45 – Open source, PyMC, and community support

01:15:20 – Advice for newcomers

01:20:55 – Future of BART, Rust, and probabilistic programming

Thank 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 Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian...

Get early access to Alex's next live-cohort courses!

Today’s clip is from episode 141 of the podcast, with Sam Witty.

Alex and Sam discuss the ChiRho project, delving into the intricacies of causal inference, particularly focusing on Do-Calculus, regression discontinuity designs, and Bayesian structural causal inference.

They explain ChiRho's design philosophy, emphasizing its modular and extensible nature, and highlights the importance of efficient estimation in causal inference, making complex statistical methods accessible to users without extensive expertise.

Get the full discussion here.

• Intro to Bayes Course (first 2 lessons free)
• Advanced Regression Course (first 2 lessons free)

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 ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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 Models

09:13 Understanding Causal Probabilistic Programming

12:10 ChiRho and Its Design Principles

15:03 ChiRho’s Functionality and Use Cases

17:55 Counterfactual Worlds and Mediation Analysis

20:47 Efficient Estimation in ChiRho

24:08 Future Directions for Causal AI

50:21 Understanding the Do-Operator in Causal Inference

56:45 ChiRho’s Role in Causal Inference and Bayesian Modeling

01:01:36 Roadmap and Future Developments for ChiRho

01:05:29 Real-World Applications of Causal Probabilistic Programming

01:10:51 Challenges in Causal Inference Adoption

01:11:50 The Importance of Causal Claims in Research

01:18:11 Bayesian Approaches to Causal Inference

01:22:08 Combining Gaussian Processes with Causal Inference

01:28:27 Future Directions in Probabilistic Programming and Causal Inference

Thank 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...