Computing Propensity Score Weights for CTA Models Involving Perfectly Predicted Endpoints

Paul R. Yarnold & Ariel Linden

Optimal Data Analysis, LLC & Linden Consulting Group, LLC

The use of CTA to construct propensity score weights is complicated by division by zero in models having any perfectly predicted endpoints: omitting undefined propensity scores yields a degenerate solution. This note presents an algorithmic remedy to this situation.

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What is Optimal Data Analysis?

Paul R. Yarnold

Optimal Data Analysis, LLC

Preview begins by describing the ODA algorithm, requisite special-purpose software, and applied investigations using ODA to conduct statistical analysis. Discussion next addresses the development and application of multivariable linear and non-linear optimal models. Preview concludes by discussing current research and development foci, including causal inference methodology, system automation, new application arenas, and evolving methods and resources.

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