Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data

• URL: http://arxiv.org/abs/2206.01931v1
• Date: Sat, 4 Jun 2022 07:48:13 GMT
• Title: Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data
• Authors: Debo Cheng (1), Jiuyong Li (1), Lin Liu (1), Kui Yu (2), Thuc Duy Lee (1), Jixue Liu (1) ((1) School of Information Technology and Mathematical Sciences, University of South Australia (2) School of Computer Science and Information Engineering, Hefei University of Technology)
• Abstract summary: Instrumental variable (IV) is a powerful approach to inferring the causal effect of a treatment on an outcome of interest from observational data. Existing IV methods require that an IV is selected and justified with domain knowledge. In this paper, we study and design a data-driven algorithm to discover valid IVs from data under mild assumptions.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Instrumental variable (IV) is a powerful approach to inferring the causal effect of a treatment on an outcome of interest from observational data even when there exist latent confounders between the treatment and the outcome. However, existing IV methods require that an IV is selected and justified with domain knowledge. An invalid IV may lead to biased estimates. Hence, discovering a valid IV is critical to the applications of IV methods. In this paper, we study and design a data-driven algorithm to discover valid IVs from data under mild assumptions. We develop the theory based on partial ancestral graphs (PAGs) to support the search for a set of candidate Ancestral IVs (AIVs), and for each possible AIV, the identification of its conditioning set. Based on the theory, we propose a data-driven algorithm to discover a pair of IVs from data. The experiments on synthetic and real-world datasets show that the developed IV discovery algorithm estimates accurate estimates of causal effects in comparison with the state-of-the-art IV based causal effect estimators.

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