JIAB-2026-Y8S7
Machine Learning for Causal Discovery in Observational Studies
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Editor
Sofia Reyes
Handling editor for this submission.
Assigned editor: Sofia Reyes
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Abstract
Causal discovery from observational data is a central challenge in empirical social science. We benchmark six constraint-based and score-based algorithms on synthetic and real-world datasets, finding that hybrid approaches consistently outperform pure constraint methods under latent confounding.
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1 manuscriptReviewers
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Henry Weber
Isabelle Dubois
Messages
2 messagesA Author Cover letter
This paper benchmarks causal discovery algorithms on social science data. We believe it offers actionable guidance for applied researchers.
E Editor Submission received
Dear Dr. Chen,
Thank you for your submission. We have assigned two reviewers and expect their reports within four weeks. We will be in touch as soon as the reviews are received.
Best regards,
The Editors
Timeline
1 events
Waiting for Reviewers
Oct 26, 2026
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