CAUSALab
CAUSALab generates, repurposes, and analyzes health data so that key decision makers—regulators, clinicians, policymakers and the public—can make more informed decisions on topics including infectious diseases, cardiovascular diseases, and cancer.
CAUSALab News
New Publication
A new paper published in Nature Mental Health used FEP-CAUSAL Collaboration data to examine comparative effectiveness of antipsychotic medications after first-episode psychosis.
Researchers found little impact of long-acting injectable (LAI) therapies on hospitalization risk in the 18 months following first-episode psychosis. However, 3-year psychotic relapse risk indicated that long-acting injectable (LAI) medications may reduce relapse and have increased benefits in vulnerable patient subgroups.
Read the Nature Mental Health article now.
James M. Robins Receives COPSS 2025 Award
James M. Robins has been awarded the 2025 Committee of Presidents of Statistical Societies (COPSS) Distinguished Achievement Award and Lectureship!
Dr. Robins was recognized by COPSS, “for helping create the modern field of causal inference; for developing ground-breaking methods for causal inference; for the analysis of missing data; for semi-and non-parametric models, and for the wide adoption of these methods in public health, clinical medicine, and the social sciences.”
See the announcement on the COPSS website.
New Role
CAUSALab researcher Elizabeth Diemer will begin a new role as Assistant Professor of Epidemiology in the Penn Medicine Department of Biostatistics, Epidemiology and Informatics at University of Pennsylvania Perelman School of Medicine!
Elizabeth will continue working with CAUSALab as a research collaborator.
19th Kolokotrones Symposium Announced
CAUSALab has announced the 19th Kolokotrones Symposium, “Building a Learning Health System: the VA-CAUSAL Enterprise.”
This hybrid symposium celebrates the 100th anniversary of research for our VA-CAUSAL collaborator, the VA Office of Research & Development.
“Building a Learning Health System: the VA-CAUSAL Enterprise” will explore how VA-CAUSAL may serve as a helpful model for future learning health systems. Speakers will describe the program infrastructure, research use cases, and policy implications. The symposium will conclude with a panel discussion on the future of research to build learning health systems.
VA-CAUSAL is a causal inference research initiative within the U.S. Veterans Health Administration. The goal of VA-CAUSAL is to help transform the VA into a learning health system that expedites the translation of research into practice and supports decision-making by patients, clinicians, and other stakeholders to improve health.
Registration open through April 2025.
2025 Course Registration Open
Registration for CAUSALab’s 2025 Summer Courses on Causal Inference is now open!
Online Prerequisite Course (March-June, 2025)
Fundamentals of Confounding Adjustment (FCA)
Week 1 (June 16-20, 2025)
Key Topics in Causal Inference (KTCI)
Advanced Confounding Adjustment (ACA)
Week 2 (June 23-27, 2025)
Combining Information for Causal Inference (CICI)
Target Trial Emulation (TTE)
All summer courses take place in Boston, Massachusetts at the Harvard T.H. Chan School of Public Health from 9:30 AM to 4:30 PM (ET) each day. Each course offers a limited number of online seats for participants to attend virtually. Please note that courses offered during the same week occur simultaneously and cannot be taken at the same time. These courses are non-degree and non-credit. Enrollment in any course is not eligible for visa sponsorship.
Each course is designed for a specific audience and has various prerequisites that participants must meet in order to attend. To understand which courses are right for you and to register, please visit CAUSALab’s course page.
Promotion
Alejandro Szmulewicz has been promoted to Assistant Professor of Epidemiology at Harvard T.H. Chan School of Public Health. Congratulations, Alejandro!
New GitHub Release
Live on GitHub! The gfoRmulaICE package implements iterative conditional expectation (ICE) estimators of the plug-in g-formula (linked below).
Both singly robust and doubly robust ICE estimators based on parametric models are available.
The package can be used to estimate survival curves under sustained treatment strategies (interventions) using longitudinal data with time-varying treatments, time-varying confounders, censoring and competing events. The interventions can be static or dynamic, and deterministic or stochastic (including threshold interventions). Both prespecified and user-defined interventions are available.
Access gfoRmulaICE on CAUSALab’s GitHub page:
https://github.com/CausalInference
New Role
Joy Shi has a new role as Assistant Professor in the MGH Mongan Institute at Massachusetts General Hospital! We are thrilled to continue working together.
18th Kolokotrones Symposium a Success
The 18th Kolokotrones Symposium, “Causal Inference for Population Mental Health,” explored the challenges of mental health research. The event featured a large in-person audience and marked the launch of CAUSALab’s event collaborator, the Population Mental Health Lab.
Speakers: Magda Cerda (NYU Langone), Andrea Danese (King’s College London), Jaimie Gradus (Boston University School of Public Health), Katherine Keyes (Columbia University Mailman School of Public Health), Karestan Koenen (Harvard T.H. Chan School of Public Health), Henning Tiemeier (Harvard T.H. Chan School of Public Health)
Read the Harvard T.H. Chan School of Public Health article about the symposium.
Emma McGee Selected for 13th Annual Women in Medicine and Science Symposium
CAUSALab researcher Emma McGee was selected to present at Mass General Brigham’s 13th Annual Women in Medicine and Science Symposium.
This Symposium highlights the achievements of women faculty and trainees affiliated with Brigham and Women’s Hospital and Mass General Hospital.
Emma’s presentation focuses on estimating the effects of estimating the effects of adjuvant bone-modifying agent on mortality for older post-menopausal women with early-stage breast cancer using target trial emulation, dynamic marginal structural models and real-world oncology data.
Promotion
CAUSALab researcher Sophia Rein has been promoted to Research Associate at Harvard T.H. Chan School of Public Health. Congrats, Sophia!
New Funding: PCORI
Project “Remote Alert Pathway to Optimize CaRe of Cardiac Implantable Electrical Devices: RAPTOR-CIED” has been awarded $30 million in research funding by Patient-Centered Outcomes Research Institute (PCORI).
This is a close partnership between CAUSALab and the Smith Center Beth Israel Deaconess Medical Center (BIDMC). CAUSALab’s Issa Dahabreh, Associate Professor at Harvard T.H. Chan School of Public Health will lead the analytic center for this study alongside project lead Daniel B. Kramer, MD, MPH, Section Head of Electrophysiology and Digital Health at the Smith Center BIDMC and Associate Professor of Medicine at Harvard Medical School.
Read the press release.
Gonzalo Martínez-Alés Awarded New Suicide Research Grant
CAUSALab & Mount Sinai Health System researcher Gonzalo Martínez-Alés was awarded a new grant by the American Foundation for Suicide Prevention in June 2024.
“Effectiveness of Pharmacological Interventions to Prevent Suicidal Behaviors Among Individuals at High Suicide Risk: A Target Trial Emulation” (Grant ECR-1-101-23) aims to apply innovative target trial emulation design using big data from EMR to investigate the relationship of medications with suicide risk.
CAUSALab Director Miguel Hernán will serve as a mentor alongside M. Mercedes Perez-Rodriguez from Mount Sinai Health System and Enrique Baca-Garcia from Universidad Autónoma de Madrid.
New Research Featured by CNN: Metformin & Pregnancy Risk
New research featured in CNN challenges past scholarship on metformin and its impact on pregnancy.
The study by Yu-Han Chiu et al published in Annals of Internal Medicine found that women with diabetes type 2 who continued using metformin as a treatment in their first trimester of pregnancy showed no increased risk for giving birth to a baby with major birth defects when compared with women who discontinued metformin.
Dr. Yu-Han Chiu, the lead author of the study, is a CAUSALab researcher collaborator and current faculty member at Penn State College of Medicine. This work was supported and advised by CAUSALab Director Miguel Hernán and Sonia Hernández-Díaz, Professor of Epidemiology and Co-Director of Harvard Program on Perinatal and Pediatric Pharmacoepidemiology (H4P) at Harvard T.H. Chan School of Public Health.
Read the full CNN article.
Read the original publications in Annals of Internal Medicine.
Pygformula for Causal Inference Published on CAUSALab GitHub
Pygformula for causal inference is now published on CAUSALab’s GitHub! This comprehensive package is the first to implement the g-formula in Python. Development team led by Postdoctoral Fellow Jing Li.
The pygformula package implements the non-iterative conditional expectation (NICE) estimator of the g-formula algorithm. The g-formula can estimate an outcome’s counterfactual mean or risk under hypothetical treatment strategies (interventions) when there is sufficient information on time-varying treatments and confounders.