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- W2033999634 abstract "Impact assessments of criminal justice interventions depend on of the what-if variety rather than on of future events. They require predictions of what would have occurred if the experimental units that were exposed to the intervention had not been exposed. These predictions, when compared with the responses of treated units, form the basis for all evaluations of program impacts. The statistical concept of strong ignorability provides a framework for addressing the internal validity of all impact assessments. When use of strongly ignorable assignment mechanisms is not possible or practicable, strategies are available for achievement of assignments that are ignorable but not strongly ignorable. Strongly ignorable assignment mechanisms are clearly desirable; some might say mandatory." @default.
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- W2033999634 title "Causal Inference as a Prediction Problem" @default.
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- W2033999634 doi "https://doi.org/10.1086/449135" @default.
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