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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.<\/jats:p>","DOI":"10.14778\/3725688.3725717","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T14:19:21Z","timestamp":1756477161000},"page":"1933-1947","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Causal DAG Summarization"],"prefix":"10.14778","volume":"18","author":[{"given":"Anna","family":"Zeng","sequence":"first","affiliation":[{"name":"CSAIL, MIT, USA"}]},{"given":"Michael","family":"Cafarella","sequence":"additional","affiliation":[{"name":"CSAIL, MIT, USA"}]},{"given":"Batya","family":"Kenig","sequence":"additional","affiliation":[{"name":"Technion, Israel"}]},{"given":"Markos","family":"Markakis","sequence":"additional","affiliation":[{"name":"CSAIL, MIT, USA"}]},{"given":"Brit","family":"Youngmann","sequence":"additional","affiliation":[{"name":"Technion, Israel"}]},{"given":"Babak","family":"Salimi","sequence":"additional","affiliation":[{"name":"University of California, San Diego, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2016. 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