Causal inference with observational data critically relies on untestable and extra-statistical assumptions that have (sometimes) testable implications. Well-known sets of assumptions that are sufficient to justify the causal interpretation of certain estimators are called identification strategies. These templates for causal analysis, however, do not perfectly map into empirical research practice. Researchers are often left in the disjunctive of either abstracting away from their particular setting to fit in the templates, risking erroneous inferences, or avoiding situations in which the templates cannot be applied, missing valuable opportunities for conducting empirical analysis. In this article, I show how directed acyclic graphs (DAGs) can help researchers to conduct empirical research and assess the quality of evidence without excessively relying on research templates. First, I offer a concise introduction to causal inference frameworks. Then I survey the arguments in the methodological literature in favor of using research templates, while either avoiding or limiting the use of causal graphical models. Third, I discuss the problems with the template model, arguing for a more flexible approach to DAGs that helps illuminating common problems in empirical settings and improving the credibility of causal claims. I demonstrate this approach in a series of worked examples, showing the gap between identification strategies as invoked by researchers and their actual applications. Finally, I conclude highlighting the benefits that routinely incorporating causal graphical models in our scientific discussions would have in terms of transparency, testability, and generativity.

翻译：利用观测数据进行因果推断的关键依赖于不可检验且超统计的假设，这些假设有时具有可检验的含义。能够充分证明某些估计量因果解释合理性的已知假设集合被称为识别策略。然而，这些因果分析模板并不能完美映射到实证研究实践中。研究者常常陷入两难境地：要么脱离具体研究背景以套用模板，冒着错误推断的风险；要么回避模板无法适用的情形，错失开展实证分析的宝贵机会。本文展示了有向无环图（DAGs）如何帮助研究者开展实证研究并评估证据质量，从而避免过度依赖研究模板。首先，我对因果推断框架进行简要介绍。接着，我梳理方法论文献中支持使用研究模板（同时避免或限制使用因果图模型）的论点。第三，我探讨模板模型存在的问题，主张采用更灵活的DAGs方法，以阐明实证情境中的常见问题并提升因果主张的可信度。我通过一系列实例演示该方法，揭示研究者所引用的识别策略与其实际应用之间的差距。最后，我总结指出，在科研讨论中系统性地纳入因果图模型将在可解释性、可检验性和生成性方面带来的益处。