Every model leaves out or distorts some factors that are causally connected to its target phenomenon—the phenomenon that it seeks to predict or explain. If we want to make predictions, and we want to base decisions on those predictions, what is it safe to omit or to simplify, and what ought a causal model to describe fully and correctly? A schematic answer: the factors that matter are those that make a difference to the target phenomenon. There are several ways to understand differencemaking. This paper advances a view as to which is the most relevant to the forecaster and the decision-maker. It turns out that the right notion of differencemaking for thinking about idealization in prediction is also the right notion for thinking about idealization in explanation; this suggests a carefully circumscribed version of Hempel’s famous thesis that there is a symmetry between explanation and prediction.

每个模型都忽略或扭曲了一些与其目标现象有因果关系的因素——它试图预测或解释的现象。如果我们想做出预测，并且我们想根据这些预测做出决策，那么省略或简化什么是安全的，因果模型应该完整和正确地描述什么？一个示意性的答案：重要的因素是那些对目标现象产生影响的因素。有几种方法可以理解差异化。本文提出了一种观点，即哪个与预测者和决策者最相关。事实证明，在预测中思考理想化的正确差异化概念也是在解释中思考理想化的正确概念；