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Register a new userPolicy Implications of Statistical Estimates: A General Bayesian Decision-Theoretic Model for Binary Outcomes
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Akisato Suzuki Dr
Publication date
2020
fields
Mathematical Statistics
and research's language is
English
Authors
Akisato Suzuki
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How should we evaluate a policys effect on the likelihood of an undesirable event, such as conflict? The conventional practice has three limitations. First, relying on statistical significance misses the fact that uncertainty is a continuous scale. Second, focusing on a standard point estimate overlooks a variation in plausible effect sizes. Third, the criterion of substantive significance is rarely explained or justified. To overcome these, my Bayesian decision-theoretic model compares the expected loss under a policy intervention with the one under no such intervention. These losses are computed as a function of a particular effect size, the probability of this effect being realized, and the ratio of the cost of an intervention to the cost of an undesirable event. The model is more practically interpretable than common statistical decision-theoretic models using the standard loss functions or the relative costs of false positives and false negatives. I exemplify my models use through three applications and provide an R package.
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