Victor Chernozhukov

The work. Double machine learning: a recipe for using lasso, random forests and neural nets to estimate a causal effect while keeping valid standard errors. Before him, machine learning gave predictions; after him, it gave economists confidence intervals. Also quantile regression methods and high-dimensional inference with Belloni and Hansen.

The case for. The committee rewarded causal inference in 2021. Chernozhukov is the bridge from that prize to the AI decade, and he is the name that lets a Susan Athey prize read as "machine learning for economics" rather than a prize for one person's range.

The case against. The method is young, the citations are still compounding, and a second causal-inference prize five years after Angrist and Imbens would be fast by the committee's habits. On Polymarket's first day he is a nine-cent name on eighty dollars of volume.

The KmikeyM angle. Double machine learning lets a black box do the prediction while the humans keep the accountability. That is the shareholder arrangement exactly: the algorithm proposes, the vote disposes.