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Activity Number: 670
Type: Contributed
Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract - #306081
Title: Causal Inference When the Intervention Is Prioritization
Author(s): Benjamin Ogorek*+ and Shannon Terry
Companies: Nationwide Insurance and Nationwide Insurance
Address: 1080 Weybridge Rd, Columbus, OH, 43220, United States
Keywords: Permutation ; Causal Inference ; Unequal probability sampling ; cycle decomposition ; Horvitz-Thompson estimator ; Prioritization

A permutation of an ordered set is a valid cause according to the Rubin Causal Model (RCM). Intuitively, such a permutation could represent a prioritization of actions to influence an outcome of interest. From the standpoint of the RCM, many permutations may be applied, and each defines a potential outcome. In this research, we develop finite-population causal inference for estimating the causal effect of a prioritization. As causal effects are relative, population summaries from both a control and treatment permutation are compared. The control prioritization need not be a random - it may arise from an established paradigm. A modified Fisher-Yates shuffle, based on the cycle decomposition of the treatment permutation, comprises the probabilistic selection mechanism for the treatment group. Those units not selected for the treatment group either align with the control permutation, or define an additional permutation distinct from both the control and treatment permutations. First-order selection probabilities are studied, and approximate inference based on the Horvitz-Thompson and Hanson-Hurwitz estimators is presented. A simulation demonstrates the utility of this approach.

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