Dharmateja Priyadaeshi Uddandarao: Your AI Predicts. It Can’t Explain Why. | Ep 1336

In this episode of The Digital Executive Podcast, host Brian Thomas sits down with Dharmateja Uddandarao, Senior Data Scientist and Statistician at Amazon, to make a distinction that most business leaders have never fully grasped — and that costs organizations millions of dollars in misattributed decisions every year. Dharmateja opens by tracing how causality became the through line of his career not by design, but by repeatedly hitting the same wall: a beautiful, high-accuracy model would ship a recommendation, a leader would ask whether the product actually caused the outcome or whether it would have happened anyway, and the model had no answer. That gap — between what happened and what would have happened — is where statistics, data science, and economics all converge, and it is the problem Dharmateja has spent his career learning to solve. He then breaks down why all three disciplines are necessary: classical econometrics has the deepest theory of identification but is often rigid; machine learning handles complexity and scale but is causally blind, happily telling you that ice cream sales cause drownings because both rise in summer; and causal inference theory provides the connective tissue — the language of counterfactuals and potential outcomes — that makes it possible to ask what the world would have looked like if you had acted differently. He illustrates this with an ad measurement example where a naive eleven-point lift becomes a credible eight-point lift once confounding is properly addressed, and walks through counterfactual forecasting as a method for estimating causal effects when no randomized experiment was possible. His close is direct: the next decade of AI is not about prediction becoming cheaper — it already is. It is about decisions becoming more intelligent. And that requires causal grounding, honest uncertainty, and economic context, not just bigger models.

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