论文标题
心理学家和社会科学家的因果研究管道和教程
A Causal Research Pipeline and Tutorial for Psychologists and Social Scientists
论文作者
论文摘要
因果关系是了解世界的科学努力的基本组成部分。不幸的是，在心理学和社会科学中，因果关系仍然是禁忌。由于越来越多的建议采用因果方法进行研究的重要性，我们重新制定了心理学研究的典型方法，以使不可避免的因果理论与其余的研究渠道协调。我们提出了一个新的过程，该过程始于从因果发现和机器学习的融合中纳入技术的发展，验证和透明的理论形式规范。然后，我们提出将完全指定的理论模型的复杂性降低到与给定目标假设相关的基本s子模型中的方法。从这里，我们确定利息量是否可以从数据中估算出来，如果是的，则建议使用半参数机器学习方法来估计因果效应。 The overall goal is the presentation of a new research pipeline which can (a) facilitate scientific inquiry compatible with the desire to test causal theories (b) encourage transparent representation of our theories as unambiguous mathematical objects, (c) to tie our statistical models to specific attributes of the theory, thus reducing under-specification problems frequently resulting from the theory-to-model gap, and (d) to yield results and estimates which are causally有意义且可再现。通过具有现实世界数据的教学示例来证明该过程，我们以摘要和局限性的讨论得出结论。
Causality is a fundamental part of the scientific endeavour to understand the world. Unfortunately, causality is still taboo in much of psychology and social science. Motivated by a growing number of recommendations for the importance of adopting causal approaches to research, we reformulate the typical approach to research in psychology to harmonize inevitably causal theories with the rest of the research pipeline. We present a new process which begins with the incorporation of techniques from the confluence of causal discovery and machine learning for the development, validation, and transparent formal specification of theories. We then present methods for reducing the complexity of the fully specified theoretical model into the fundamental submodel relevant to a given target hypothesis. From here, we establish whether or not the quantity of interest is estimable from the data, and if so, propose the use of semi-parametric machine learning methods for the estimation of causal effects. The overall goal is the presentation of a new research pipeline which can (a) facilitate scientific inquiry compatible with the desire to test causal theories (b) encourage transparent representation of our theories as unambiguous mathematical objects, (c) to tie our statistical models to specific attributes of the theory, thus reducing under-specification problems frequently resulting from the theory-to-model gap, and (d) to yield results and estimates which are causally meaningful and reproducible. The process is demonstrated through didactic examples with real-world data, and we conclude with a summary and discussion of limitations.
