Evaluating decider models: randomize everything, warns researcher as IR concepts get rediscovered
antoine_chaffin · x · 2026-10-31
In an X exchange, antoinechaffin argues that when evaluating decider models you should randomize everything: evaluations contain many exploitable signals that simply don't hold on real traffic.
The thread also jokes that research is cyclical — with IR (information retrieval) becoming cool again, the field may be rederiving concepts like propensity scores and counterfactual learning-to-rank step by step. Chaffin adds that much decider-model work is essentially about avoiding the gotchas the community fell for over the past few years.
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