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Most ML models answer one question: what is likely to happen? The harder question is what will change if you intervene. That gap is where causal reasoning begins.

In this episode, we explore how constraint-based algorithms learn causal structure directly from data, and how LLMs can step in to resolve what statistics alone cannot.

You’ll learn:

* How PC, FCI, and RFCI discover causal graphs using conditional independence tests, and what assumptions each one makes.

* How to encode domain knowledge as hard constraints, so the algorithm stops producing edges that are statistically plausible but practically nonsensical.

* How LLMs can review and refine the output graph, resolving ambiguous orientations with domain reasoning when the data runs out of signal.

By the end, you’ll have a clear picture of a three-layer pipeline that combines statistical discovery, expert constraints, and LLM review into a coherent approach to causal graph learning.

If you’d rather read than listen, the full article (with diagrams, code examples, and implementation details) is available on Substack:

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