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S1E08. Imagining the Impossible: How Counterfactuals Shape Our World
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: [email protected]
🌐 Website: https://pozapas.github.io/
🔗 LinkedIn: https://www.linkedin.com/in/amir-rafe-08770854/
🐦 X: https://x.com/rafeamir
View all episodes
By Amir RafeSeason 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: [email protected]
🌐 Website: https://pozapas.github.io/
🔗 LinkedIn: https://www.linkedin.com/in/amir-rafe-08770854/
🐦 X: https://x.com/rafeamir