The most common approach to causal modelling is the potential outcomes framework due to Neyman and Rubin. In this framework, outcomes of counterfactual treatments are assumed to be well-defined. This metaphysical assumption is often thought to be problematic yet indispensable. The conventional approach relies not only on counterfactuals, but also on abstract notions of distributions and assumptions of independence that are not directly testable. In this paper, we construe causal inference as treatment-wise predictions for finite populations where all assumptions are testable; this means that one can not only test predictions themselves (without any fundamental problem), but also investigate sources of error when they fail. The new framework highlights the model-dependence of causal claims as well as the difference between statistical and scientific inference.

翻译：最常见的因果建模方法是Neyman和Rubin提出的潜在结果框架。该框架假设反事实处理的结果是明确定义的。这种形而上学假设常被认为存在问题却又不可或缺。传统方法不仅依赖反事实，还依赖于不可直接检验的抽象分布概念和独立性假设。本文提出将因果推断理解为针对有限总体的处理方式预测，其中所有假设均可检验；这意味着人们不仅能检验预测本身（不存在任何根本性问题），还能在预测失败时探究误差来源。新框架凸显了因果主张的模型依赖性，以及统计推断与科学推断之间的差异。