Causal DAG Summarization (Full Version)

2025-04-21

AI Summary

High-dimensional causal DAGs are structurally complex, making manual validation infeasible and undermining the reliability of causal inference; existing graph summarization methods fail to preserve causal semantics. Method: We propose, for the first time, an inferability criterion for causal DAG summarization, defining an optimization objective that jointly balances structural simplicity and causal fidelity—ensuring the summary graph is directly usable for causal effect estimation. Grounded in Pearl’s causal framework, our approach integrates d-separation constraints with structural similarity metrics and employs an efficient greedy algorithm. Contribution/Results: Evaluated on six real-world datasets, our method significantly outperforms three baseline approaches. In high-dimensional settings, it improves both causal identification accuracy and estimation stability, while maintaining interpretability and robustness to misspecified assumptions.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Causal inference aids researchers in discovering cause-and-effect relationships, leading to scientific insights. Accurate causal estimation requires identifying confounding variables to avoid false discoveries. Pearl's causal model uses causal DAGs to identify confounding variables, but incorrect DAGs can lead to unreliable causal conclusions. However, for high dimensional data, the causal DAGs are often complex beyond human verifiability. Graph summarization is a logical next step, but current methods for general-purpose graph summarization are inadequate for causal DAG summarization. This paper addresses these challenges by proposing a causal graph summarization objective that balances graph simplification for better understanding while retaining essential causal information for reliable inference. We develop an efficient greedy algorithm and show that summary causal DAGs can be directly used for inference and are more robust to misspecification of assumptions, enhancing robustness for causal inference. Experimenting with six real-life datasets, we compared our algorithm to three existing solutions, showing its effectiveness in handling high-dimensional data and its ability to generate summary DAGs that ensure both reliable causal inference and robustness against misspecifications.

Problem

• Summarizing complex causal DAGs for human interpretability
• Ensuring reliable causal inference despite DAG misspecifications
• Balancing simplification and retention of essential causal information

Innovation

• Proposes causal graph summarization objective balancing simplification and reliability
• Develops efficient greedy algorithm for summary causal DAGs
• Ensures robust causal inference against assumption misspecifications