Observational Data for Heterogeneous Treatment Effects with Application to Recommender Systems

Akos Lada
Akos Lada
Alexander Peysakhovich
Alexander Peysakhovich
Diego Aparicio
Diego Aparicio

Proceedings of the 2019 ACM Conference on Economics and Computation, pp. 199-213, 2019.

Cited by: 0|Bibtex|Views4|DOI:https://doi.org/10.1145/3328526.3329558
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Other Links: dl.acm.org|dblp.uni-trier.de|academic.microsoft.com

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 amount...More

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