Ancestral Instrument Method for Causal Inference without Complete Knowledge

Debo Cheng, Jiuyong Li, Lin Liu, Jiji Zhang, Thuc duy Le, Jixue Liu

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Unobserved confounding is the main obstacle to causal effect estimation from observational data. Instrumental variables (IVs) are widely used for causal effect estimation when there exist latent confounders. With the standard IV method, when a given IV is valid, unbiased estimation can be obtained, but the validity requirement on a standard IV is strict and untestable. Conditional IVs have been proposed to relax the requirement of standard IVs by conditioning on a set of observed variables (known as a conditioning set for a conditional IV). However, the criterion for finding a conditioning set for a conditional IV needs a directed acyclic graph (DAG) representing the causal relationships of both observed and unobserved variables. This makes it challenging to discover a conditioning set directly from data. In this paper, by leveraging maximal ancestral graphs (MAGs) for causal inference with latent variables, we study the graphical properties of ancestral IVs, a type of conditional IVs using MAGs, and develop the theory to support data-driven discovery of the conditioning set for a given ancestral IV in data under the pretreatment variable assumption. Based on the theory, we develop an algorithm for unbiased causal effect estimation with a given ancestral IV and observational data. Extensive experiments on synthetic and real-world datasets demonstrate the performance of the algorithm in comparison with existing IV methods.

Original languageEnglish
Title of host publicationProceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022
EditorsLuc De Raedt
PublisherInternational Joint Conferences on Artificial Intelligence
Pages4843-4849
Number of pages7
ISBN (Electronic)9781956792003
DOIs
Publication statusPublished - 23 Jul 2022
Event31th International Joint Conference on Artificial Intelligence, IJCAI 2022 - Messe Wien, Vienna, Austria
Duration: 23 Jul 202229 Jul 2022
https://ijcai-22.org/
https://www.ijcai.org/proceedings/2022/

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference31th International Joint Conference on Artificial Intelligence, IJCAI 2022
Country/TerritoryAustria
CityMesse Wien, Vienna
Period23/07/2229/07/22
Internet address

Scopus Subject Areas

  • Artificial Intelligence

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