The Northwestern Law School in Chicago, IL is holding their 14th annual workshop on Research Design for Causal Inference.
Workshop Overview: (Monday – Friday, July 28 – August 1, 2025)
The workshop will cover the design of true randomized experiments and contrast them to natural or quasi experiments and to pure observational studies, where part of the sample is treated, the remainder is a control group, but the researcher controls neither which units are treated vs. control, nor administration of the treatment. It will assess the causal inferences one can draw from specific “causal” research designs, threats to valid causal inference, and research designs that can mitigate those threats.
Most empirical methods courses survey a variety of methods. The workshop will begin instead with the goal of causal inference, and how to design a research plan to come closer to that goal, using messy, real-world datasets. The methods are often adapted to a particular study.
Advanced Workshop Overview: (Monday – Wednesday, August 4-6, 2025)
The advanced workshop provides in-depth discussion of selected topics that are beyond what can be covered in the main workshop. The principal topics for 2025 are application of machine learning methods to causal inference; difference-in-differences methods for staggered treatments (applied to different units at different times); and advanced instrumental variable methods.
Target Audience for Main Workshop:
Quantitative empirical researchers (including faculty, graduate students, post-docs, and other
researchers) in social science, including law, political science, economics, many business-school areas (finance, accounting, management, marketing, etc.), medicine, sociology, education, psychology, etc. –anywhere that causal inference is important.
Target Audience for Advanced Workshop: Empirical researchers who are familiar with the basics of causal inference (from our main workshop or otherwise), and want to extend their knowledge.
There will also be a Zoom option, but in person attendance is preferred.
Get more information here