A new causal disentanglement objective for recommendation models improves out-of-distribution generalization under policy-induced distribution shift, with production A/B gains despite offline parity.
URL https://www.nature.com/articles/ s44387-026-00105-2
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Causal Representation Learning for Generalisable Recommendation
A new causal disentanglement objective for recommendation models improves out-of-distribution generalization under policy-induced distribution shift, with production A/B gains despite offline parity.