Variance-based difference between graphical identification conditions of causal effects in linear structural equation models

IF 0.7 4区数学 Q3 STATISTICS & PROBABILITY

Statistics & Probability Letters Pub Date : 2026-02-01 Epub Date: 2025-10-10 DOI:10.1016/j.spl.2025.110561

Chie Taguchi , Manabu Kuroki

引用次数: 0

Abstract

In the context of statistical causal inference using linear structural equation models, researchers in the field of artificial intelligence and statistical science have developed several identification conditions for evaluating causal effects. However, there are some scenarios where several identification conditions can be applied simultaneously to estimate causal effects. To enhance estimation accuracy, we focus on five key identification conditions: the back-door criterion, the front-door criterion, the front-door-like criterion, the conditional instrumental variable condition, and the effect restoration condition. We then compare these five identification conditions in terms of estimation accuracy (asymptotic variance) and conclude that, in some cases, the qualitative comparison of estimation accuracy among these identification conditions can be directly assessed from the graphical structure, even before statistical data are collected.

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线性结构方程模型中因果效应图形识别条件的方差差异

在利用线性结构方程模型进行统计因果推理的背景下，人工智能和统计科学领域的研究人员开发了几种评估因果效应的识别条件。然而，在某些情况下，可以同时应用几个识别条件来估计因果效应。为了提高估计精度，我们重点研究了五个关键的识别条件：后门准则、前门准则、类前门准则、条件工具变量条件和效果恢复条件。然后，我们在估计精度（渐近方差）方面比较了这五种识别条件，并得出结论，在某些情况下，甚至在收集统计数据之前，可以直接从图形结构中评估这些识别条件之间估计精度的定性比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistics & Probability Letters 数学-统计学与概率论

CiteScore

1.60

自引率

0.00%

发文量

173

审稿时长

6 months

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