BriefGPT.xyz
Jun, 2024
潜在混淆变量的总效应在总结性因果图中的可识别性:基于前门准则的扩展
Toward identifiability of total effects in summary causal graphs with latent confounders: an extension of the front-door criterion
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Charles K. Assaad
TL;DR
使用摘要因果图从观测数据中确定总效应的充分图形条件,即使存在隐藏的混杂和没有足够的变量集进行调整,也有助于从观测数据中理解和估计因果效应的持续努力。
Abstract
Conducting experiments to estimate
total effects
can be challenging due to cost, ethical concerns, or practical limitations. As an alternative, researchers often rely on
causal graphs
to determine if it is possib
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