Discovering Ancestral Instrumental Variables for Causal Inference From Observational Data

Area of Science:

• Causal inference
• Observational data analysis
• Statistical methodology

Background:

• Instrumental variable (IV) methods estimate causal effects from observational data, even with unobserved confounders.
• Current IV methods rely on domain expertise for selecting valid IVs, which can be a bottleneck and source of bias.
• Discovering valid IVs is crucial for reliable causal effect estimation.

Purpose of the Study:

• To develop a data-driven algorithm for discovering valid instrumental variables (IVs).
• To address the limitations of existing IV methods that require manual IV selection.
• To enable accurate causal effect estimation from observational data without strong prior domain knowledge.

Main Methods:

• Utilized partial ancestral graphs (PAGs) to develop theory for identifying candidate ancestral IVs (AIVs).
• Designed an algorithm to search for a set of AIVs and their conditioning sets from data.
• Proposed a data-driven approach to discover a pair of IVs.

Main Results:

• The developed algorithm successfully discovers valid IVs from data under mild assumptions.
• Experimental results on synthetic and real-world datasets demonstrate accurate causal effect estimation.
• The proposed method outperforms existing state-of-the-art IV-based causal effect estimators.

Conclusions:

• A novel data-driven algorithm for instrumental variable discovery has been successfully developed.
• The algorithm, grounded in partial ancestral graph theory, enables robust causal effect estimation.
• This approach enhances the applicability and reliability of IV methods in observational studies.