Description
Back to topCausal inference plays a foundational role across the social, health, and environmental sciences, and continues to evolve as real-world applications grow more complex. This workshop will bring together researchers and practitioners advancing both methodology and applications to address challenges such as the design of complex experiments, overt and hidden bias in observational studies, data integration from diverse sources, treatment effect heterogeneity, and the use of AI to scale and enhance causal analysis. By engaging experts from statistics, biostatistics, epidemiology, economics, computer science, and related fields, the workshop will highlight recent progress, foster cross-disciplinary dialogue, and inspire new directions in the design and analysis of causal studies in modern, data-rich settings. Insights from these discussions will advance evidence-based policy making and support decision-making in health, economics, the social sciences, and beyond.
In-Person Registration
Seats are limited at the venue, which means that in-person registration may be capped prior to the workshop start date. If capacity is reached, a waitlist will be imposed, which the registration form will reflect. Early registration is strongly encouraged.
All in-person registrants must wait to receive an invitation to attend in-person from IMSI before traveling, which generally begin to be sent out 4-6 weeks in advance.
All registrants (online and in-person) will receive zoom links and are welcome to attend online.
Registration Fee
A non-refundable registration fee will be payable by credit card or debit card for any participants invited to attend this workshop in-person. In-person participants agree to pay the non-refundable fee by the deadline given by IMSI. Failure to pay the fee by the deadline may mean that the invitation to attend in-person is revoked.
Current fees:
- $25 for students
- $50 for non-students