#141 AI Assisted Causal Inference, with Sam Witty

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Sep 18, 2025

In this engaging discussion, Sam Whitty, the founder of Sorbus AI and a pioneer in causal probabilistic programming, dives into the intricacies of causal inference. He explores his journey from engineering to developing ChiRho, a language that merges mechanistic and data-driven models. Listeners will learn about counterfactual reasoning, the significance of modular design, and practical applications in science and engineering. Sam emphasizes the need for collaboration in transforming causal questions into actionable insights, while also looking ahead at the future of causal AI.

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ANECDOTE

Engineer's Path Into Causal Modeling

Sam started as a mechanical engineer and did energy efficiency consulting where he unknowingly performed causal inference.
That experience motivated him to bridge mechanistic engineering models with probabilistic machine learning.

INSIGHT

Why Hybrid Mechanistic+Data Models Matter

Mechanistic models extrapolate well because they encode basic physical laws, but they omit complexities best learned from data.
Combining mechanistic structure with probabilistic learning preserves extrapolation while improving fit to real-world data.

INSIGHT

What Causal Probabilistic Programming Is

Causal probabilistic programming adds an intervention operator to probabilistic programs so you can ask what-if questions about actions.
It mechanizes interventions and counterfactual reasoning, turning causal problems into probabilistic inference tasks.

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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!

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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

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