The goal of much observational research is to identify risk factors that have a causal effect on health and social outcomes. However, observational data are subject to biases from confounding, selection and measurement, which can result in an underestimate or overestimate of the effect of interest. Various advanced statistical approaches exist that offer certain advantages in terms of addressing these potential biases. However, although these statistical approaches have different underlying statistical assumptions, in practice they cannot always completely remove key sources of bias; therefore, using design-based approaches to improve causal inference is also important. Here it is the design of the study that addresses the problem of potential bias – either by ensuring it is not present (under certain assumptions) or by comparing results across methods with different sources and direction of potential bias. The distinction between statistical and design-based approaches is not an absolute one, but it provides a framework for triangulation – the thoughtful application of multiple approaches (e.g. statistical and design based), each with their own strengths and weaknesses, and in particular sources and directions of bias. It is unlikely that any single method can provide a definite answer to a causal question, but the triangulation of evidence provided by different approaches can provide a stronger basis for causal inference. Triangulation can be considered part of wider efforts to improve the transparency and robustness of scientific research, and the wider scientific infrastructure and system of incentives.

中文翻译：

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利用观察数据进行因果推断：证据三角测量的必要性


许多观察性研究的目标是确定对健康和社会结果有因果影响的风险因素。然而，观察数据容易受到混杂、选择和测量带来的偏差，这可能导致低估或高估感兴趣的影响。存在各种先进的统计方法，它们在解决这些潜在偏差方面具有一定的优势。然而，尽管这些统计方法具有不同的基本统计假设，但在实践中它们并不总是能够完全消除关键的偏差来源；因此，使用基于设计的方法来改进因果推理也很重要。这里的研究设计解决了潜在偏差问题——要么确保潜在偏差不存在（在某些假设下），要么通过比较具有不同潜在偏差来源和方向的方法的结果。统计方法和基于设计的方法之间的区别并不是绝对的，但它提供了一个三角测量的框架——多种方法（例如基于统计和设计的方法）的深思熟虑的应用，每种方法都有自己的优点和缺点，特别是来源和方法。偏向的方向。任何单一方法不太可能为因果问题提供明确的答案，但不同方法提供的证据三角测量可以为因果推理提供更强有力的基础。三角测量可以被视为提高科学研究透明度和稳健性以及更广泛的科学基础设施和激励制度的更广泛努力的一部分。