Season 1: The Book of Why

As Season 1 comes to a close, we explore the convergence of big data, artificial intelligence, and the age-old question of “why.” While machines have become astonishingly good at pattern recognition, they still struggle with the essence of human understanding: causal reasoning. In this episode, we reflect on how the Causal Revolution challenged traditional statistics and ask whether AI can ever truly emulate human curiosity and imagination.

What does it take for machines to not only predict outcomes but explain them? Can we teach AI to distinguish correlation from causation—or even reason about counterfactuals? Join us for a thought-provoking finale as we examine the future of causal thinking in a world increasingly driven by data and algorithms.

🔍 Stay Connected

📧 Email: ⁠⁠⁠⁠amir.rafe@usu.edu⁠⁠⁠⁠

🌐 Website: ⁠⁠⁠⁠https://pozapas.github.io/⁠⁠⁠⁠

🔗 LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/amir-rafe-08770854/⁠⁠⁠⁠

🐦 X: ⁠⁠⁠⁠https://x.com/rafeamir⁠⁠

Season 1: The Book of Why

What lies between cause and effect? In this episode, we delve into the concept of mediation, the hidden pathways that connect actions to outcomes. From James Lind’s battle with scurvy to groundbreaking diagrams in intelligence research, we explore how scientists uncover the mechanisms that explain how and why effects occur. When we ask, “Does Drug B prevent heart attacks?”, we’re really asking: “Through what chain of events?” Understanding these chains can mean the difference between truth and tragic error.

We also reflect on the legacy of Barbara Burks—a pioneering woman in science who challenged the norms of her time by visualizing the complex threads of nature and nurture through path diagrams, decades ahead of her field. Why do some causes work directly while others weave through mediators? And how can this knowledge transform scientific policy, medicine, and artificial intelligence?

Join us as we climb deeper into the Ladder of Causation and learn to trace the steps between action and consequence.

🔍 Stay Connected

📧 Email: ⁠⁠⁠amir.rafe@usu.edu⁠⁠⁠

🌐 Website: ⁠⁠⁠https://pozapas.github.io/⁠⁠⁠

🔗 LinkedIn: ⁠⁠⁠https://www.linkedin.com/in/amir-rafe-08770854/⁠⁠⁠

🐦 X: ⁠⁠⁠https://x.com/rafeamir⁠⁠

Season 1: The Book of Why

What if Cleopatra’s nose had been shorter? What if Joe had taken the aspirin? In this episode, we climb to the top rung of the Ladder of Causation and explore the fascinating world of counterfactuals, alternate realities that help us understand what is and what could have been. We’ll examine how imagining different scenarios is more than philosophical musing, it's central to assigning blame, making predictions, and defining responsibility. From climate change attribution to legal causation, we uncover how counterfactual reasoning powers everything from scientific discovery to everyday decision-making.

We also compare two powerful approaches to counterfactuals: the structural causal models that give us a precise computational framework, and the potential outcomes model rooted in statistics. What makes one more effective than the other? And how can machines learn to think in “what ifs” the way humans do?

🔍 Stay Connected

📧 Email: ⁠⁠amir.rafe@usu.edu⁠⁠

🌐 Website: ⁠⁠https://pozapas.github.io/⁠⁠

🔗 LinkedIn: ⁠⁠https://www.linkedin.com/in/amir-rafe-08770854/⁠⁠

🐦 X: ⁠⁠https://x.com/rafeamir⁠⁠