Causal Inference with Conditional Instruments using Deep Generative Models

• URL: http://arxiv.org/abs/2211.16246v1
• Date: Tue, 29 Nov 2022 14:31:54 GMT
• Title: Causal Inference with Conditional Instruments using Deep Generative Models
• Authors: Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu and Thuc Duy Le
• Abstract summary: A standard IV is expected to be related to the treatment variable and independent of all other variables in the system. conditional IV (CIV) method has been proposed to allow a variable to be an instrument conditioning on a set of variables. We propose to learn the representations of a CIV and its conditioning set from data with latent confounders for average causal effect estimation.
• Score: 21.771832598942677
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a treatment on an outcome of interest from observational data with latent confounders. A standard IV is expected to be related to the treatment variable and independent of all other variables in the system. However, it is challenging to search for a standard IV from data directly due to the strict conditions. The conditional IV (CIV) method has been proposed to allow a variable to be an instrument conditioning on a set of variables, allowing a wider choice of possible IVs and enabling broader practical applications of the IV approach. Nevertheless, there is not a data-driven method to discover a CIV and its conditioning set directly from data. To fill this gap, in this paper, we propose to learn the representations of the information of a CIV and its conditioning set from data with latent confounders for average causal effect estimation. By taking advantage of deep generative models, we develop a novel data-driven approach for simultaneously learning the representation of a CIV from measured variables and generating the representation of its conditioning set given measured variables. Extensive experiments on synthetic and real-world datasets show that our method outperforms the existing IV methods.

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