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Episode 53: Beyond Correlation: How Causal AI Is Teaching Machines to Reason
What if AI could explain why things happen—not just predict what comes next?
In this episode of AI Ignited, host Elif Baser sits down with Ilkem Kaya to explore Causal AI and data-driven decision making. Moving beyond pattern recognition, they unpack how causal inference allows AI systems to simulate interventions, evaluate policy impact, and make more reliable decisions in fields like public health, climate modeling, and economics. From Judea Pearl’s causal frameworks to real-world risks of misapplied models, this conversation challenges the idea that correlation is enough. If AI is shaping decisions that affect society, shouldn’t it understand cause and effect first?
In this episode of AI Ignited, host Elif Baser sits down with Ilkem Kaya to explore Causal AI and data-driven decision making. Moving beyond pattern recognition, they unpack how causal inference allows AI systems to simulate interventions, evaluate policy impact, and make more reliable decisions in fields like public health, climate modeling, and economics. From Judea Pearl’s causal frameworks to real-world risks of misapplied models, this conversation challenges the idea that correlation is enough. If AI is shaping decisions that affect society, shouldn’t it understand cause and effect first?
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By Elif BaşerWhat if AI could explain why things happen—not just predict what comes next?
In this episode of AI Ignited, host Elif Baser sits down with Ilkem Kaya to explore Causal AI and data-driven decision making. Moving beyond pattern recognition, they unpack how causal inference allows AI systems to simulate interventions, evaluate policy impact, and make more reliable decisions in fields like public health, climate modeling, and economics. From Judea Pearl’s causal frameworks to real-world risks of misapplied models, this conversation challenges the idea that correlation is enough. If AI is shaping decisions that affect society, shouldn’t it understand cause and effect first?
In this episode of AI Ignited, host Elif Baser sits down with Ilkem Kaya to explore Causal AI and data-driven decision making. Moving beyond pattern recognition, they unpack how causal inference allows AI systems to simulate interventions, evaluate policy impact, and make more reliable decisions in fields like public health, climate modeling, and economics. From Judea Pearl’s causal frameworks to real-world risks of misapplied models, this conversation challenges the idea that correlation is enough. If AI is shaping decisions that affect society, shouldn’t it understand cause and effect first?