评估反事实世界中的模型误差，提升决策可信度。
Assessing model error in counterfactual worlds
- 提出三种估计反事实模型误差的方法
- 仿真实验验证各方法的优劣与适用条件
- 为可评估的场景设计提供关键建议
反事实情景建模是未来规划中最常见的方法之一，常以‘如果……会怎样？’的形式出现。尽管广泛用于决策，但情景预测很少被事后评估。预测与实际观测的差异源于情景偏差和模型校准不当，我们主张后者对评估模型在决策中的价值更为关键，但需估计反事实世界中的模型误差。本文对比了三种误差估计方法，并通过仿真实验展示其优势与局限。同时提出反事实误差估计的推荐方案，并讨论使情景预测可评估所需的关键场景设计要素。
原文摘要 · Abstract (English)
Counterfactual scenario modeling exercises that ask "what would happen if?" are one of the most common ways we plan for the future. Despite their ubiquity in planning and decision making, scenario projections are rarely evaluated retrospectively. Differences between projections and observations come from two sources: scenario deviation and model miscalibration. We argue the latter is most important for assessing the value of models in decision making, but requires estimating model error in counterfactual worlds. Here we present and contrast three approaches for estimating this error, and demonstrate the benefits and limitations of each in a simulation experiment. We provide recommendations for the estimation of counterfactual error and discuss the components of scenario design that are required to make scenario projections evaluable.
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