A Causal Research Pipeline and Tutorial for Psychologists and Social Scientists
面向心理学与社会科学的因果研究流程及教程AI 解读 ↓
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面向心理学与社会科学的因果研究流程及教程
- 研究问题
- 心理学和社会科学如何把因果理论转化为透明的数学模型，并将理论建构、目标假设、可识别性判断与因果效应估计连成一致流程？
- 方法与思路
- 作者先结合因果发现和机器学习构建并形式化理论，再提取目标假设所需子模型、判断可估计性，最后使用半参数机器学习估计效应。
- 材料陈述的贡献
- 摘要提出面向因果理论的研究流程，强调理论表达透明、统计模型对应理论属性以及结果可复现，并通过真实数据教学示例展示。
- 阅读关注点
- 核对因果发现与理论验证各自的假设、简化子模型是否保留识别信息，以及从可估计性判断到半参数估计的具体实现和限制。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未列出具体算法和识别条件，教学示例不能证明流程在所有社会科学问题中有效。
原文摘要
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.