Why machine learning "succeeds" in development but fails in deployment

Google research recently uncovered a key reason for real-world failures of conventional ML systems. The problem is that many widely varying models fit the data equally well — oftentimes, it’s left to chance which model is selected. We explain why Causal AI promises a better way to specify models that perform in the real world.

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This paper introduces a key failure mode for conventional machine learning pipelines, and illustrates why Causal AI helps towards solving the problem.