Metaphysics and modern AI: What is causality?
Tue Jan 27 2026
In this episode of our series about Metaphysics and modern AI, we break causality down to first principles and explain how to tell factual mechanisms from convincing correlations. From gold-standard Randomized Control Trials (RCT) to natural experiments and counterfactuals, we map the tools that build trustworthy models and safer AI.
Defining causes, effects, and common causal structuresGestalt theory: Why correlation misleads and how pattern-seeking tricks usStatistical association vs causal explanationRCTs and why randomization mattersNatural experiments as ethical, scalable alternativesJudea Pearl’s do-calculus, counterfactuals, and first-principles modelsLimits of causality, sample size, and inferenceBuilding resilient AI with causal grounding and governance
This is the fourth episode in our metaphysics series. Each topic in the series is leading to the fundamental question, "Should AI try to think?"
Check out previous episodes:
Series IntroWhat is reality?What is space and time?If conversations like this sharpen your curiosity and help you think more clearly about complex systems, then step away from your keyboard and enjoy this journey with us.
What did you think? Let us know.
Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:
LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.
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In this episode of our series about Metaphysics and modern AI, we break causality down to first principles and explain how to tell factual mechanisms from convincing correlations. From gold-standard Randomized Control Trials (RCT) to natural experiments and counterfactuals, we map the tools that build trustworthy models and safer AI. Defining causes, effects, and common causal structuresGestalt theory: Why correlation misleads and how pattern-seeking tricks usStatistical association vs causal explanationRCTs and why randomization mattersNatural experiments as ethical, scalable alternativesJudea Pearl’s do-calculus, counterfactuals, and first-principles modelsLimits of causality, sample size, and inferenceBuilding resilient AI with causal grounding and governance This is the fourth episode in our metaphysics series. Each topic in the series is leading to the fundamental question, "Should AI try to think?" Check out previous episodes: Series IntroWhat is reality?What is space and time?If conversations like this sharpen your curiosity and help you think more clearly about complex systems, then step away from your keyboard and enjoy this journey with us. What did you think? Let us know. Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.