Policy Implications of Statistical Estimates: A General Bayesian Decision-Theoretic Model for Binary Outcomes
Akisato Suzuki
备注：
This is the Accepted Manuscript version of an article published by Taylor & Francis in Statistics and Public Policy on April 25, 2022, available with open access at: https://doi.org/10.1080/2330443X.2022.2050328. For reference, the published version should be consulted
发表信息：
Statistics and Public Policy 9, no.1 (2022): 85-96
How should we evaluate the effect of a policy on the likelihood of an undesirable event, such as conflict? The significance test 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 the variation in plausible effect sizes. Third, the criterion of substantive significance is rarely explained or justified. A new Bayesian decision-theoretic model, "causal binary loss function model," overcomes these issues. It compares the expected loss under a policy intervention with the one under no intervention. These losses are computed based on a particular range of the effect sizes of a policy, the probability mass of this effect size range, the cost of the policy, and the cost of the undesirable event the policy intends to address. The model is more applicable than common statistical decision-theoretic models using the standard loss functions or capturing costs in terms of false positives and false negatives. I exemplify the model's use through three applications and provide an R package.
Policy learning under constraint: Maximizing a primary outcome while controlling an adverse event
Inria PreMeDICaL, Inserm, University of Montpellier（法国国家信息与自动化研究所PreMeDICaL团队，法国国家健康与医学研究院，蒙彼利埃大学）；Elixir Health, Paris（Elixir Health公司）；Université Paris Cité, CNRS, MAP5（巴黎西岱大学，法国国家科学研究中心MAP5实验室）
Wasserstein Policy Learning for Distributional Outcomes
School of Computing and Information Technology, Great Bay University（广东大湾大学计算机与信息科技学院）；Department of Data Science, City University of Hong Kong（香港城市大学数据科学系）；School of Statistics and Data Science & Institute of Big Data Research, Shanghai University of Finance and Economics（上海财经大学统计与数据科学学院及大数据研究院）
Targeted Active Learning for Preference-Based Treatment Effects on Multivariate Outcomes
Theremia；Paris Brain Institute (ICM)（巴黎脑科学研究所（ICM））；Sorbonne Université（索邦大学）；Inserm（法国国家健康与医学研究院）；CNRS（法国国家科学研究中心）；AP-HP（巴黎公共援助医院）；Inria（法国国家信息与自动化研究所）；Université de Montpellier（蒙彼利埃大学）；Université Paris Cité（巴黎西岱大学）；Centre for Research in Epidemiology and Statistics (CRESS)（流行病学与统计学研究中心（CRESS））
