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- W3122439647 abstract "Decision makers in health, public policy, technology, and social science are increasingly interested in going beyond ‘one-size-fits-all’ policies to personalized ones. Thus, they are faced with the problem of estimating heterogeneous causal effects. Unfortunately, estimating heterogeneous effects from randomized data requires large amounts of statistical power and while observational data is often available in much larger quantities the presence of unobserved confounders can make using estimates derived from it highly suspect. We show that under some assumptions estimated heterogeneous treatment effects from observational data can preserve the rank ordering of the true heterogeneous causal effects. Such an approach is useful when observational data is large, the set of features is high-dimensional, and our priors about feature importance are weak. We probe theeffectiveness of our approach in simulations and show a real-world example in a large-scale recommendations problem." @default.
- W3122439647 created "2021-02-01" @default.
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- W3122439647 date "2018-01-01" @default.
- W3122439647 modified "2023-10-03" @default.
- W3122439647 title "Predicting Heterogeneous Treatment Effects in Ranking Systems" @default.
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- W3122439647 doi "https://doi.org/10.2139/ssrn.3190359" @default.
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