Document Type
Article
Publication Title
Entropy
Abstract
This paper investigates the problem of selecting instrumental variables relative to a target causal influence X → Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders. We propose a necessary condition for detecting variables that cannot serve as instrumental variables. Unlike many existing conditions for continuous variables, i.e., that at least two or more valid instrumental variables are present in the system, our condition is designed with a single instrumental variable. We then characterize the graphical implications of our condition in linear non-Gaussian acyclic causal models. Given that the existing graphical criteria for the instrument validity are not directly testable given observational data, we further show whether and how such graphical criteria can be checked by exploiting our condition. Finally, we develop a method to select the set of candidate instrumental variables given observational data. Experimental results on both synthetic and real-world data show the effectiveness of the proposed method. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
DOI
10.3390/e24040512
Publication Date
4-5-2022
Keywords
causal discovery, causal graph, instrumental variable, non-Gaussinaity
Recommended Citation
F. Xie, Y. He, Z. Geng, Z. Chen, R. Hou, and K. Zhang, "Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models", Entropy, vol. 24, no. 4: 512, Apr 2022, doi: 10.3390/e24040512
Comments
Open Access version with thanks to MDPI License: CC BY 4.0 Uploaded 30 March 2022