Credible causal inference beyond toy models
超越玩具模型的可信因果推断AI 解读 ↓
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超越玩具模型的可信因果推断
- 研究问题
- 经验研究的实际情境难以完全匹配标准识别策略时，如何借助因果图明确假设并提高因果主张的可信度？
- 方法与思路
- 作者介绍因果推断框架，梳理支持识别策略模板的文献论证，再通过多个实例讨论灵活使用有向无环图。
- 材料陈述的贡献
- 摘要强调识别策略的名称与实际应用可能存在差距，并主张常规使用因果图有助于透明表达、可检验性及研究拓展。
- 阅读关注点
- 应核对各实例中图结构对应的实质假设、可检验含义及识别结论，并区分因果图的表达作用与证据支持程度。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；摘要主要呈现方法论论证，不能据此确认新估计量或识别假设得到验证。
原文摘要
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.