We describe a simple yet rigorous graphical method for eliminating bias in theory-based program evaluation. The method is an application to social and international development program evaluation of the graphical causal models used to test medical treatments. We implement a graphical causal model for the World Bank’s well-known Bangladesh Integrated Nutrition Project. We show how to construct the graphical causal model to represent program theory in context in explicitly causal terms. We then show how to visually inspect the graphical causal model to distinguish causal from non-causal associations between variables in evaluation data. Finally, we show how to select a set of adjustment variables to neutralize non-causal associations, eliminating bias in all forms of causal inference—qualitative and quantitative, linear and non-linear.

中文翻译：

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基于理论的评估中因果程序归因的图解方法

我们描述了一种简单而严格的图形方法，用于消除基于理论的项目评估中的偏差。该方法是对用于测试医疗的图形因果模型的社会和国际发展计划评估的应用。我们为世界银行著名的孟加拉国综合营养项目实施了图形因果模型。我们展示了如何构建图形因果模型来以明确的因果术语表示上下文中的程序理论。然后，我们展示如何直观地检查图形因果模型，以区分评估数据中变量之间的因果关联和非因果关联。最后，我们展示了如何选择一组调整变量来中和非因果关联，消除所有形式的因果推理中的偏差——定性和定量、线性和非线性。