因果推理的纵向设计的未实现的承诺

Collabra Pub Date : 2023-01-01 DOI:10.1525/collabra.89142
Wen Wei Loh, Dongning Ren
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引用次数: 0

摘要

纵向设计常用于心理学研究。一种直观的分析方法是在估计焦点预测因子(即治疗)对结果的影响时,对先前的测量进行调整,以支持因果结论的有效性。这种方法是常规应用,但很少在实践中得到证实。调整以前的测量值意味着什么?它一定能改善因果推论吗?在本文中,我们证明了这些问题的答案远非直截了当。我们解释如何调整以前的测量可以减少或诱导偏差在常见的纵向方案。我们进一步证明,在因果假设不太严格的情况下,调整或不调整先前的测量结果可能会以某种方式引起偏差。换句话说,即使在最简单的情况下,调整或不调整先前的测量结果也会同时加强或破坏纵向研究的因果推论。我们敦促研究人员克服使用纵向设计来测试因果关系所带来的毫无根据的自满情绪。提出了加强心理学研究中因果结论的实用建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Unfulfilled Promise of Longitudinal Designs for Causal Inference
Longitudinal designs are frequently used in psychological research. An intuitive analytic approach is to adjust for previous measurements to bolster the validity of causal conclusions when estimating the effect of a focal predictor (i.e., treatment) on an outcome. This approach is routinely applied but rarely substantiated in practice. What are the implications of adjusting for previous measurements? Does it necessarily improve causal inferences? In this paper, we demonstrate that answers to these questions are far from straightforward. We explain how adjusting for previous measurements can reduce or induce bias in common longitudinal scenarios. We further demonstrate, in scenarios with less stringent causal assumptions, adjusting or not adjusting for previous measurements can induce bias one way or the other. Put differently, adjusting or not adjusting for a previous measurement can simultaneously strengthen and undermine causal inferences from longitudinal research, even in the simplest scenarios. We urge researchers to overcome the unwarranted complacency brought on by using longitudinal designs to test causality. Practical recommendations for strengthening causal conclusions in psychology research are provided.
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