What Markov boundary offers to tabular prediction.</p>\n","updatedAt":"2026-06-01T04:04:49.323Z","author":{"_id":"63e8882e4577a86987ad91c2","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/CmgkIiJ9Unu1HlPtp7TqF.png","fullname":"Shu","name":"Shuwan","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":1,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9288749098777771},"editors":["Shuwan"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/CmgkIiJ9Unu1HlPtp7TqF.png"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2605.29411","authors":[{"_id":"6a18f67a56b4bb14ec65ce56","name":"Shu Wan","hidden":false},{"_id":"6a18f67a56b4bb14ec65ce57","name":"Abhinav Gorantla","hidden":false},{"_id":"6a18f67a56b4bb14ec65ce58","name":"Huan Liu","hidden":false},{"_id":"6a18f67a56b4bb14ec65ce59","name":"K. Selçuk Candan","hidden":false}],"publishedAt":"2026-05-28T00:00:00.000Z","submittedOnDailyAt":"2026-06-01T00:00:00.000Z","title":"The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction","submittedOnDailyBy":{"_id":"63e8882e4577a86987ad91c2","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/CmgkIiJ9Unu1HlPtp7TqF.png","isPro":false,"fullname":"Shu","user":"Shuwan","type":"user","name":"Shuwan"},"summary":"Under standard graphical assumptions, the Markov boundary of a target variable is the smallest set of features that renders every other feature redundant. Once the boundary is observed, the target is conditionally independent of the rest of the table. This is a tempting object for tabular prediction, since it names exactly the columns a model should need. Yet modern regressors are still trained on the full feature set. We ask whether the Markov boundary is genuinely useful for prediction on SCM3K, a 3,450-task synthetic SCM benchmark with feature counts from 40 to 1000 and six SCM families, evaluated with six regressors. The answer is more nuanced than the theory suggests. Restricting a regressor to the oracle boundary often improves prediction substantially, and the improvement grows as the feature space becomes larger and sparser. But the natural pipeline of recovering the boundary with causal discovery and training on the recovered mask does not deliver. Existing estimators exhaust the compute budget before reaching the regime where the boundary helps most, and even where they run they rarely beat the full feature set. We trace this to three causes. Discovery optimizes structural recovery rather than prediction. False negatives and false positives carry sharply asymmetric predictive cost. The exact boundary is only one of many feature sets that beat all features. We then develop what these facts imply for prediction-aligned feature selection and for tabular models that learn to use causal structure.","upvotes":1,"discussionId":"6a18f67a56b4bb14ec65ce5a","ai_summary":"Research examines the practical effectiveness of Markov boundaries in tabular prediction, finding that while theoretically optimal, current causal discovery methods fail to consistently improve predictive performance due to computational limitations and mismatched optimization goals.","ai_keywords":["Markov boundary","causal discovery","structural recovery","feature selection","tabular prediction","synthetic causal models","SCM3K benchmark"],"organization":{"_id":"68f18e87566fa1e381f22889","name":"CSE472-blanket-challenge","fullname":"Beyond the Blanket","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/63e8882e4577a86987ad91c2/IHpkkl-4b0TWpmyFl6g-b.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"63e8882e4577a86987ad91c2","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/CmgkIiJ9Unu1HlPtp7TqF.png","isPro":false,"fullname":"Shu","user":"Shuwan","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"organization":{"_id":"68f18e87566fa1e381f22889","name":"CSE472-blanket-challenge","fullname":"Beyond the Blanket","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/63e8882e4577a86987ad91c2/IHpkkl-4b0TWpmyFl6g-b.png"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2605/2605.29411.md"}">
The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction
Published on May 28
· Submitted by Shu on Jun 1 Abstract
Research examines the practical effectiveness of Markov boundaries in tabular prediction, finding that while theoretically optimal, current causal discovery methods fail to consistently improve predictive performance due to computational limitations and mismatched optimization goals.
AI-generated summary
Under standard graphical assumptions, the Markov boundary of a target variable is the smallest set of features that renders every other feature redundant. Once the boundary is observed, the target is conditionally independent of the rest of the table. This is a tempting object for tabular prediction, since it names exactly the columns a model should need. Yet modern regressors are still trained on the full feature set. We ask whether the Markov boundary is genuinely useful for prediction on SCM3K, a 3,450-task synthetic SCM benchmark with feature counts from 40 to 1000 and six SCM families, evaluated with six regressors. The answer is more nuanced than the theory suggests. Restricting a regressor to the oracle boundary often improves prediction substantially, and the improvement grows as the feature space becomes larger and sparser. But the natural pipeline of recovering the boundary with causal discovery and training on the recovered mask does not deliver. Existing estimators exhaust the compute budget before reaching the regime where the boundary helps most, and even where they run they rarely beat the full feature set. We trace this to three causes. Discovery optimizes structural recovery rather than prediction. False negatives and false positives carry sharply asymmetric predictive cost. The exact boundary is only one of many feature sets that beat all features. We then develop what these facts imply for prediction-aligned feature selection and for tabular models that learn to use causal structure.
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What Markov boundary offers to tabular prediction.
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