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- W4289754487 abstract "Heterogeneous treatment effects can be very important in the analysis of randomized clinical trials. Heightened risks or enhanced benefits may exist for particular subsets of study subjects. When the heterogeneous treatment effects are specified as the research is being designed, there are proper and readily available analysis techniques. When the heterogeneous treatment effects are inductively obtained as an experiment's data are analyzed, significant complications are introduced. There can be a need for special loss functions designed to find local average treatment effects and for techniques that properly address post selection statistical inference. In this paper, we tackle both while undertaking a recursive partitioning analysis of a randomized clinical trial testing whether individuals on probation, who are low risk, can be minimally supervised with no increase in recidivism." @default.
- W4289754487 created "2022-08-04" @default.
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- W4289754487 date "2018-07-11" @default.
- W4289754487 modified "2023-10-01" @default.
- W4289754487 title "Using Recursive Partitioning to Find and Estimate Heterogenous Treatment Effects In Randomized Clinical Trials" @default.
- W4289754487 doi "https://doi.org/10.48550/arxiv.1807.04164" @default.
- W4289754487 hasPublicationYear "2018" @default.
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