June 10-11: Causal Machine Learning for Health Economics and Outcomes Research - Virtual
event-Short-Courses

June 10, 2026 - June 11, 2026

Discover How Causal Machine Learning Is Transforming Early Health Technology Assessment

As the cost of developing new health technologies continues to rise, developers and investors are increasingly turning to data-driven methods to identify innovations with the greatest clinical and market potential. This course introduces the principles of causal machine learning (CML) and its applications in early-stage health technology assessment (HTA) and translational health economics. Participants will explore how CML can help prioritize research and development efforts, improve data efficiency, and support better decision making across funding, regulatory, and reimbursement contexts.

Technical Topics Include:
• Fundamentals of causal inference and machine learning in health economics
• Applications of CML in early-stage technology evaluation and prioritization
• Translational health economics methods to support R&D investment decisions
• Integrating CML into HTA frameworks to strengthen predictive and explanatory power
• Common challenges, data limitations, and validation strategies

This Course Includes Practical Tools and Concepts That Can Be Immediately Applied, Including:
• Real-world case examples illustrating CML’s role in health technology development
• Guided exercises on building a research strategy using causal modeling techniques
• Insights for applying CML in early product evaluation, funding, and pricing decisions

 

PREREQUISITE: Familiarity with key elements, methods, language, and basic health technology principles are prerequisites to attending this course.

Registration coming soon!

LEVEL: Intermediate 
TRACK: Methodological & Statistical Research

Faculty

Noemi Kreif, PhD
Assistant Professor
University of Washington
Department of Pharmacy 
Seattle, WA, USA

Julia Hatamyar, PhD
Research Fellow
University of York
York, England, UK

David Glynn, PhD
Research Fellow and Assistant Professor
University of York
York, England, UK

Schedule:

LENGTH: 4 Hours | Course runs 2 consecutive days, 2 hours each day

Wednesday, 10 June 2026 | Course runs 2 consecutive days, 2 hours per day
11:00AM–1:00PM Eastern Daylight Time (EDT)
8:00AM–10:00AM Pacific Daylight Time (PDT)
16:00PM–18:00PM British Summer Time (BST)
15:00PM–17:00PM Coordinated Universal Time (UTC)
17:00PM–19:00PM Central European Summer Time (CEST)

Thursday, 11 June 2026 | Course runs 2 consecutive days, 2 hours per day
11:00AM–1:00PM Eastern Daylight Time (EDT)
8:00AM–10:00AM Pacific Daylight Time (PDT)
16:00PM–18:00PM British Summer Time (BST)
15:00PM–17:00PM Coordinated Universal Time (UTC)
17:00PM–19:00PM Central European Summer Time (CEST)

Registration coming soon!

Back to all short courses

ISPOR short courses are designed to enhance knowledge and techniques in core health economics and outcomes research (HEOR) topics as well as emerging trends in the field. Short courses offer 4 or 8 hours of premium scientific education and an electronic course book. Active attendee participation combined with our expert faculty creates an immersive and impactful learning experience. Short courses are not recorded and are only available during the live course presentation.

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