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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 Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Joshua Meehl, Javier Sabio, Kristian Higgins, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık, Suyog Chandramouli and Adam Tilmar Jakobsen.
- Intro to Bayes Course (first 2 lessons free)
- Advanced Regression Course (first 2 lessons free)
Links from the show:
- Sam’s website: https://samwitty.github.io/
- Sam on LinkedIn: https://www.linkedin.com/in/sam-witty-46708572/
- Sam on GitHub: https://github.com/SamWitty
- ChiRho docs: https://basisresearch.github.io/chirho/getting_started.html
- Causal Inference using Gaussian Processes with Structured Latent Confounders: https://proceedings.mlr.press/v119/witty20a/witty20a.pdf
- Automated Efficient Estimation using Monte Carlo Efficient Influence Functions: https://proceedings.neurips.cc/paper_files/paper/2024/file/1d10fe211f5139de49f94c6f0c7cecbe-Paper-Conference.pdf
- PhD Thesis: https://samwitty.github.io/papers/Witty_Dissertation.pdf
- LBS #137 Causal AI & Generative Models, with Robert Ness: https://learnbayesstats.com/episode/137-causal-ai-generative-models-robert-ness
Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
