Higher-Order Least Squares: Assessing Partial Goodness of Fit of Linear Causal Models

被引：5

作者：

Schultheiss, Christoph ^{[1
]}

Buhlmann, Peter ^{[1
]}

Yuan, Ming ^{[2
]}

机构：

[1] Swiss Fed Inst Technol, Seminar Stat, Zurich, Switzerland

[2] Columbia Univ, Dept Stat, New York, NY USA

来源：

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | 2024年 / 119卷 / 546期

基金：

欧洲研究理事会;

关键词：

Causal inference; Latent confounding; Model misspecification; Nodewise regression; Structural equation models; INFERENCE; IDENTIFICATION; DISCOVERY; TESTS;

D O I：

10.1080/01621459.2022.2157728

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We introduce a simple diagnostic test for assessing the overall or partial goodness of fit of a linear causal model with errors being independent of the covariates. In particular, we consider situations where hidden confounding is potentially present. We develop a method and discuss its capability to distinguish between covariates that are confounded with the response by latent variables and those that are not. Thus, we provide a test and methodology for partial goodness of fit. The test is based on comparing a novel higher-order least squares principle with ordinary least squares. In spite of its simplicity, the proposed method is extremely general and is also proven to be valid for high-dimensional settings. for this article are available online.

引用

页码：1019 / 1031

页数：13

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