Logging the features and relative probability of one unexposed item alongside the exposed item identifies causal effects of content features from stochastic algorithms even with unobserved confounders.
Lost in aggrega- tion: The causal interpretation of the iv estimand.arXiv preprint arXiv:2601.12120,
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Estimating Causal Effects from Data Generated by Stochastic Algorithms
Logging the features and relative probability of one unexposed item alongside the exposed item identifies causal effects of content features from stochastic algorithms even with unobserved confounders.