Causal Inference with Conditional Instruments using Deep Generative Models
This work addresses a gap in causal inference for researchers and practitioners by enabling data-driven discovery of CIVs, which is incremental as it builds on existing CIV theory with a novel implementation.
The paper tackles the challenge of discovering conditional instrumental variables (CIVs) and their conditioning sets from observational data with latent confounders, proposing a deep generative model-based approach that outperforms existing IV methods in experiments on synthetic and real-world datasets.
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.