Ancestral Instrument Method for Causal Inference without Complete Knowledge
This work addresses the problem of causal inference for researchers and practitioners in fields like epidemiology and social sciences by providing a more flexible method, though it is incremental as it builds on existing IV and MAG frameworks.
The paper tackles the challenge of causal effect estimation with unobserved confounders by introducing ancestral instrumental variables (IVs) that relax strict validity requirements, enabling data-driven discovery of conditioning sets and achieving unbiased estimation as demonstrated in experiments.
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.