Economic historians have long been aware of many forms of bias that could lead to spurious causal inferences. However, our approach to these biases has been muddled at times by dealing with each bias separately and by confusion about the sources of bias and how to mitigate them. This paper shows how the methodology of directed acyclical graphs (DAGs) formulated by Pearl (2009) and particularly the concept of collider bias can provide economic historians with a unified approach to managing a wide range of biases that can distort causal inference. I present ten examples of collider bias drawn from economic history research, focussing mainly on examples where the authors were able to overcome or mitigate the bias. Thus, the paper addresses how to diagnose collider bias and also strategies for managing it. The paper also shows that quasi-random experimental designs are rarely able to overcome collider bias. Although all of these biases were understood by economic historians before, conceptualising them as collider bias will improve economic historians’ understanding of the limitations of particular sources and help us develop better research designs in the future.

经济史学家早就意识到可能会导致虚假因果推论的多种形式的偏见。但是，我们有时会通过分别处理每个偏差以及对偏差的来源以及如何缓解它们的困惑而使我们对这些偏差的方法感到困惑。本文展示了Pearl（2009）提出的有向无环图（DAG）方法，尤其是对撞机偏向的概念如何为经济史学家提供统一的方法来管理各种可能扭曲因果推论的偏向。我提供了十个从经济史研究中得出的对撞机偏向的例子，主要集中在作者能够克服或减轻偏向的例子上。因此，本文讨论了如何诊断对撞机偏见以及管理它的策略。该论文还表明，准随机实验设计很少能够克服对撞机偏差。尽管以前经济史学家已经理解了所有这些偏见，但将它们概念化为对撞者偏见将提高经济史学家对特定来源局限性的理解，并有助于我们将来开发更好的研究设计。