Knowledge-Informed Local Causal Discovery of Optimal Adjustment Sets

2026-07-05

AI Summary

This work addresses the challenge of identifying optimal adjustment sets in data-scarce settings, where local causal discovery is hindered by insufficient samples, incomplete neighborhoods, and unresolved Markov equivalence classes. To overcome these limitations, the authors propose b-LOAD, a novel method that uniquely integrates structured prior knowledge directly into the local causal discovery process. By leveraging constrained edge information and dynamically expanding the local graph boundary via Meek rules, b-LOAD constructs a knowledge-constrained local partially directed acyclic graph. This approach monotonically refines the admissible equivalence class, substantially broadening the scope of identifiable causal queries and recovering optimal adjustment sets that are otherwise unidentifiable from observational data alone. Experimental results demonstrate that b-LOAD significantly outperforms purely data-driven and conventional knowledge-enhanced baselines under data scarcity and structural complexity, with validation on real biological networks confirming its efficacy and robustness.

Abstract

Local causal discovery is a scalable alternative to global structure learning. However, it can struggle to identify valid adjustment sets in data-scarce settings because of finite-sample uncertainty, incomplete local neighborhoods, and unresolved Markov equivalence. Although many application domains provide structured background knowledge, its integration into local causal discovery remains limited. We propose b-LOAD, a knowledge-informed extension of the LOAD algorithm for local discovery of optimal adjustment sets. b-LOAD incorporates prior edge constraints directly into the local structure-learning procedure and uses Meek's rules to expand the discovery frontier dynamically, yielding a knowledge-constrained partially directed graph over the relevant local subgraph. This strategy helps prevent structurally relevant nodes introduced by prior knowledge from being excluded by local search. We prove that, under sound background knowledge, the procedure monotonically refines the admissible equivalence class and can enlarge the set of identifiable causal queries, enabling recovery of optimal adjustment sets that are not identifiable from observational conditional-independence information alone. Empirically, b-LOAD improves downstream causal effect estimation relative to purely data-driven and standard knowledge-augmented baselines, particularly in data-scarce and structurally complex regimes. Results on real-world biological networks show that locally targeted prior knowledge provides the largest gains and remains beneficial under moderate structural noise. These findings position b-LOAD as a scalable approach for converting fragmented domain knowledge into more reliable causal-effect estimation.

Problem

• local causal discovery
• optimal adjustment sets
• background knowledge
• data scarcity
• Markov equivalence

Innovation

• knowledge-informed causal discovery
• optimal adjustment sets
• local causal structure learning
• Meek's rules
• Markov equivalence class