Higher-Order Least Squares: Assessing Partial Goodness of Fit of Linear Causal Models
OPEN ACCESS
Loading...
Author / Producer
Date
2024
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Abstract
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.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
119 (546)
Pages / Article No.
1019 - 1031
Publisher
Taylor & Francis
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Causal inference; Latent confounding; Model misspecification; Nodewise regression; Structural equation models
Organisational unit
03502 - Bühlmann, Peter L. / Bühlmann, Peter L.
Notes
Funding
786461 - Statistics, Prediction and Causality for Large-Scale Data (EC)
Related publications and datasets
Is new version of: