David M. Haas

According to our database, David M. Haas authored at least 5 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

A novel two-sample Mendelian randomization framework integrating common and rare variants: application to assess the effect of HDL-C on preeclampsia risk.

Yu Zhang, Ming Li, David M. Haas, C. Noel Bairey Merz, Tsegaselassie Workalemahu, Kelli Ryckman, Janet M. Catov, Lisa D. Levine, Alexa Freedman, George R. Saade, Jiaqi Hu, Hongyu Zhao, Xihao Li, Nianjun Liu, Qi Yan

Briefings Bioinform., 2026

2025

LLMs for Causal Reasoning in Medicine? A Call for Caution.

Saurabh Mathur, Ranveer Singh, Michael A. Skinner, Predrag Radivojac, David M. Haas, Lakshmi Raman, Sriraam Natarajan

Proceedings of the 13th ACM IKDD International Conference on Data Science, 2025

2024

Modeling Multiple Adverse Pregnancy Outcomes: Learning from Diverse Data Sources.

Saurabh Mathur, Veerendra P. Gadekar, Rashika Ramola, Peixin Wang, Ramachandran Thiruvengadam, David M. Haas, Shinjini Bhatnagar, Nitya Wadhwa, Garbhini Study Group, Predrag Radivojac, Himanshu Sinha, Kristian Kersting, Sriraam Natarajan

Proceedings of the Artificial Intelligence in Medicine - 22nd International Conference, 2024

2022

Explaining Deep Tractable Probabilistic Models: The sum-product network case.

Athresh Karanam, Saurabh Mathur, Predrag Radivojac, David M. Haas, Kristian Kersting, Sriraam Natarajan

Proceedings of the International Conference on Probabilistic Graphical Models, 2022

2021

A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes.

Athresh Karanam, Alexander L. Hayes, Harsha Kokel, David M. Haas, Predrag Radivojac, Sriraam Natarajan

Proceedings of the Artificial Intelligence in Medicine, 2021