Causal DAG Summarization (Full Version)
This addresses the challenge for researchers in causal inference who need to verify and use complex causal DAGs in high-dimensional settings, representing an incremental improvement over existing graph summarization methods.
The paper tackles the problem of complex causal DAGs in high-dimensional data being unverifiable by humans, proposing a causal graph summarization method that balances simplification with retaining essential causal information. Experiments on six real-life datasets show the method generates summary DAGs that ensure reliable causal inference and robustness against misspecifications.
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.