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CHAUN achieves a remarkable 25.6% improvement in QINI scores, redefining the benchmarks for uplift modeling in the presence of unobserved confounding.
Transformers, like ReLU networks, are universal approximators, and their expressive power for piecewise linear functions grows exponentially with depth.
