# Causal Inference Approaches for Profiling ### Presenting at the STS Annual Meeting 2025 In her talk, “Improving Risk Adjustment in the Assessment of Congenital Heart Center Surgical Quality," Dr. Normand explained how modern causal inference techniques can offer a more accurate assessment of center performance. [ Read more arrow\_circle\_right ](https://www.sts.org/news/enhancing-patient-outcomes-new-approach-risk-adjustment-and-center-performance-evaluation?utm_source=Dynamics%20365%20Customer%20Insights%20-%20Journeys&utm_medium=email&utm_term=N%2FA&utm_campaign=E-Bulletin%20DAY%202-%2001.25.25&utm_content=STS%2025%20E-Bulletin%20%28DAY%202%29%20-%2001.25.25#msdynmkt_trackingcontext=83853587-2065-4806-8edc-2d8b50e8794d&msdynmkt_prefill=mktprf68a6ade55f784ae8a8137f3460381901eoprf) ![SLN presenting STS CHSD research at STS Annual Meeting 2025](/sites/g/files/omnuum9296/files/styles/hwp_1_1__480x480/public/2025-03/SLN%20presenting%20STS%202025_1.jpg?itok=uODMbVhk) ## expand\_more ## **Modern Analytics to Improve Quality & Outcome Assessments Following Congenital Heart Surgery.** [5R01HL162893-02](https://reporter.nih.gov/search/XtFjYN8510mb_6ljAa4ReA/project-details/10641880) 06/2022-05/2026 Annually more than 40,000 children undergo surgery for congenital heart defects (CHD) in the US and while patient outcomes have improved over the past 30 years, morbidity and mortality remain high for some children, and significant variation exists across the more than 100 US congenital heart centers. Using a registry containing detailed clinical data from virtually all U.S. congenital heart centers, we will develop machine learning and causal inference approaches to best utilize the diversity of the data to improve CHD surgical quality assessments. We expect the proposed research will transform our capabilities for assessing CHD surgical quality and drive national improvements in outcomes for children and families impacted by CHD. Download 1 citation download- [BibTeX](/bibcite/export?pager_style=no_pager&number_of_items=12&sort_field=bibcite_year--desc&&content_filter%5B0%5D=722051&&format=bibtex) - [EndNote X3 XML](/bibcite/export?pager_style=no_pager&number_of_items=12&sort_field=bibcite_year--desc&&content_filter%5B0%5D=722051&&format=endnote8) - [EndNote 7 XML](/bibcite/export?pager_style=no_pager&number_of_items=12&sort_field=bibcite_year--desc&&content_filter%5B0%5D=722051&&format=endnote7) - [Endnote tagged](/bibcite/export?pager_style=no_pager&number_of_items=12&sort_field=bibcite_year--desc&&content_filter%5B0%5D=722051&&format=tagged) - [Marc](/bibcite/export?pager_style=no_pager&number_of_items=12&sort_field=bibcite_year--desc&&content_filter%5B0%5D=722051&&format=marc) - [PubMedId](/bibcite/export?pager_style=no_pager&number_of_items=12&sort_field=bibcite_year--desc&&content_filter%5B0%5D=722051&&format=pubmed_id) - [RIS](/bibcite/export?pager_style=no_pager&number_of_items=12&sort_field=bibcite_year--desc&&content_filter%5B0%5D=722051&&format=ris) ### 2024 Yi Liu, Alexander W Levis, Sharon-Lise Normand, and Larry Han. 2024. “[Multi-Source Conformal Inference Under Distribution Shift.](/publication/multi-source-conformal-inference-under-distribution-shift)”. Proceedings of Machine Learning Research, 235, Pp. 31344-82 Yi Liu, Alexander W Levis, Sharon-Lise Normand, and Larry Han. 2024. “[Multi-Source Conformal Inference Under Distribution Shift.](/publication/multi-source-conformal-inference-under-distribution-shift)”. Proceedings of Machine Learning Research, 235, Pp. 31344-82 - add\_circle do\_not\_disturb\_on Abstract Recent years have experienced increasing utilization of complex machine learning models across multiple sources of data to inform more generalizable decision-making. However, distribution shifts across data sources and privacy concerns related to sharing... ## Presentations 2024 American Causal Inference Conference, May 2024 in Seattle, WA. Larry Han oral presentation, “Data fusion for enhanced generalizability/transportability, efficiency, and heterogeneous treatment effect estimation”; Sharon-Lise Normand presented a plenary talk, “Causal Inference in the Trenches: Challenges and Opportunities.” [https://sci-info.org/wp-content/uploads/2024/05/event\_202312\_agenda\_pdf\_aoggo.pdf](https://sci-info.org/wp-content/uploads/2024/05/event_202312_agenda_pdf_aoggo.pdf) 2nd Global Symposium of Research Methodology Innovation in Trauma and Emergency Care, May 2024 in Columbus, OH. Presented keynote address, “Modern Causal Inference Approaches for Health Services.” [https://nationwidechildrens.cloud-cme.com/course/courseoverview?P=0&EID=30692](https://nationwidechildrens.cloud-cme.com/course/courseoverview?P=0&EID=30692) 2024 Applied Statistics Symposium, June 2024 in Nashville, TN. Yi Liu presented, “Multi-source conformal inference under distribution shift.” 2024 International Conference on Machine Learning, July 2024 in Vienna, Austria. Larry Han poster presentation, “Multi-Source Conformal Inference Under Distribution Shift” June 2024, Sharon-Lise Normand presented a Biostatistics Seminar at NYU Grossman School of Medicine, “Modern Causal Inference Approaches for Assessing Healthcare Quality.” 2024 Joint Statistical Meeting, August 2024 in Portland, OR. Larry Han presented, “Multi-Source Conformal Inference Under Distribution Shift.” Sep 2024, Larry Han presented a Biostatistics Seminar at NYU Grossman School of Medicine, "Multi-Source Conformal Inference Under Distribution Shift." 2025 STS Annual Meeting, January 2025 in Los Angeles, CA. Meena Nathan presented, “Richard E. Clark Memorial Paper for Congenital Heart Surgery: Understanding Mortality Following Congenital Heart Surgery: What Do Procedure-Specific Factors Add?” Sharon-Lise Normand presented, “Improving Risk Adjustment in the Assessment of Congenital Heart Center Surgical Quality.” April 2025, Sharon-Lise Normand presented, "Causal Inference Approaches to Assessing Healthcare Providers," at the European Causal Inference Conference in Ghent, Belgium. May 2025, Sharon-Lise Normand presenting, "Modern Causal Inference Approaches to Profiling Healthcare Providers" at the at the [2025 Lifetime Data Science (LiDS) Conference](https://community.amstat.org/lids/events/2025lids-conf) in Brooklyn, NY. See also:- [ Current Projects ](/page-categories/current-projects) ## Study Team [### Haley Abing ](/people/haley-abing) Senior Program Manager, Harvard Medical School ![Haley Abing](/sites/g/files/omnuum9296/files/styles/hwp_4_5__690x865/public/2025-03/Haley-Abing.jpg?itok=c58NHTKU) [### Ben Buzzee ](/people/ben-buzzee) Data Analyst, Harvard Medical School ![Ben Buzzee](/sites/g/files/omnuum9296/files/styles/hwp_4_5__690x865/public/2025-04/BenB_Headshot.jpg?itok=BUz5xXfK) [### Larry Han ](/people/larry-han) Professor, Northeastern University ![Prof. Larry Han](/sites/g/files/omnuum9296/files/styles/hwp_4_5__690x865/public/2025-03/Larry%20Han_headshot.png?itok=EK0X80UJ) [### John Mayer ](/people/john-mayer) Senior Associate in Cardiac Surgery, Boston Children's Hospital Professor of Surgery, Harvard Medical School ![Dr. John Mayer](/sites/g/files/omnuum9296/files/styles/hwp_4_5__690x865/public/2025-03/John%20Mayer_%20photo%20IMG_8864.jpg?itok=VipsS8-D) [### Meena Nathan ](/people/meena-nathan) Cardiac Surgeon, Boston Children’s Hospital ![Dr. Meena Nathan](/sites/g/files/omnuum9296/files/styles/hwp_4_5__690x865/public/2025-03/Meena%20Nathan.png?itok=W7ArpUq8) [### Sara Pasquali ](/people/sara-pasquali) Professor, Congenital Heart Center, C.S. Mott Children’S Hospital, University of Michigan Medical School ![Dr. Sara Pasquali](/sites/g/files/omnuum9296/files/styles/hwp_4_5__690x865/public/2025-03/Sara%20Pasquali_headshot%202.jpeg?itok=LUFjh18i) [### Katya Zelevinsky ](/people/katya-zelevinsky) Biostatistician, Harvard Medical School ![Katya Zelevinsky](/sites/g/files/omnuum9296/files/styles/hwp_4_5__690x865/public/2025-03/katya_zelevinsky_headshot.jpg?itok=dI-3IdBP)