Collaborative network
The Tao of RWD sits between methodology and practice. We translate causal-inference scholarship into workflows that researchers, regulators, and clinicians can actually run.
The project behaves like a shared workspace: causal questions are specified with domain experts, translated with methodologists, and handed off through practical run-at-home analysis workflows. A core stewardship team holds the project line and maintains the tools. A wider network of faculty, methods researchers, and platform collaborators contributes to specific scientific cases and methodological strands.
Stewardship
Co-founder & Maintainer
Founder and Principal of The Tao of RWD and Adjunct Professor at the University of Utah School of Medicine. Andy bridges causal inference methodology with practical application in regulatory and healthcare settings, with a focus on making rigorous real-world evidence workflows usable.
Maintainer
Physician and senior clinical scientist working at the intersection of AI, real-world evidence, and decision making in life sciences. Her work helps us frame clinical questions, evidence standards, and AI-enabled workflows before deployment.
Applied scientific partners
Clinical and faculty collaborators help stress-test the work against real scientific questions, data constraints, and interpretation needs.
Associate Professor, Boise State University
Nurse scientist and Fellow of the American Academy of Nursing, nationally recognized for applying machine learning to pressure injury prevention in critical care. We are collaborating with Dr. Alderden to develop predictive models for pressure injury risk stratification using real-world clinical data.
Collaboration
Associate Professor of Radiation Oncology, Columbia University Irving Medical Center
Radiation oncology researcher at Columbia Center for Radiological Research whose work connects radiation biology, oncology, mathematical modeling, machine learning, and causal inference, with more than 140 peer-reviewed publications. His collaboration helps ground the time-to-event and run-at-home analytics workflow in a real scientific use case.
Causal methods community
Methodologists and educators keep the tools grounded in identification, sensitivity, targeted learning, dynamic treatment regimes, and accessible teaching.
Causal Inference Expert, HEC Montreal & ETS Montreal
International expert in causal inference and data science based in Montreal, Canada. Justin focuses on moving beyond correlation to identify true cause-and-effect relationships and supports the teaching backbone behind the Navigator.
President, Precision Analytics
Accredited statistician and founder of Precision Analytics, a Montreal-based health and life-science analytics firm. Kathryn has more than twenty peer-reviewed publications, consults across areas from genomics to clinical research, leads statistical technology development, and co-organizes Montreal R-Ladies.
Researcher specializing in causal inference and reinforcement learning. Her work on bridging these fields inspired our reformulation of the Causal Navigator around identification, decisions, and dynamic strategies.
Researcher in reinforcement learning and causal inference, with a focus on novel methods for identifying optimal treatment strategies in dynamic treatment regimes.
Causal inference researcher and creator of Variacle, an interactive tool for exploring causal methods. His work focuses on causal inference with implied interventions and targeted learning approaches.
Coordinator, Computational Data Science, Utah Valley University
Mathematician, statistical consultant, and professor of computer science. Brian coordinates the computational data science program at Utah Valley University and contributes perspective on sensitivity analysis for causal inference.
Research focus
Implementation partners
Platform collaborators help translate rigorous causal ideas into workflows that can be tested, taught, and eventually deployed responsibly.
CEO & Co-Founder, Lucidity Sciences
Data scientist and entrepreneur whose work bridges novel mathematics with practical machine learning. Through Lucidity Sciences and the Lumawarp engine, we are exploring next-generation ML approaches for real-world evidence problems in health economics and outcomes research.
Past stewardship
People who helped shape The Tao of RWD and are no longer active in day-to-day stewardship.
Co-founder
Aimee Harrison holds a BS from MIT and an MFA from the University of Washington. She co-founded The Tao of RWD and is currently a product manager at Pedestal Health.
Contact
Questions, collaborations, and suggestions are welcome.