SCI Shortcourses will take place at the 2026 American Causal Inference Conference (ACIC), held in Salt Lake City, UT, on Tuesday, May 12th. Additionally, a virtual shortcourse will be held on May 6th.
RATES
Student Member – $80
Non-Student Member – $110
Student Non-Member – $180
Non-Student Non-Member – $210
Short Course Workshops
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Robust Methods for Non-Regular Causal Inference
VIRTUAL
May 6, 2026
10:00AM – 2:00PM Eastern Time
Description:
Modern causal inference increasingly integrates flexible machine learning, optimization, and graph-based discovery methods. However, standard inferential procedures typically rely on regularity conditions—such as asymptotic normality—that often fail in realistic, data-driven, or high-dimensional settings. This short course provides a unified framework for nonregular inference, where estimators deviate from classical asymptotic behavior, and introduces practical methods ensuring valid uncertainty quantification even under slow convergence of nuisance estimators, weak identification, or data-dependent selection. Read more
INSTRUCTORS:
Beyond the Average Treatment Effect: Estimating the causal effects of binary, categorical, continuous, and multivariate exposures in R
May 11, 2026
8:30AM-12:30PM
Description:
We tend to be most familiar with estimating the effects of binary treatments or exposures. The classic average treatment effect (ATE), risk difference, risk ratio, and odds ratio are all examples of this. However, the exposure may be more complicated than a simple binary variable. For example, there may be multiple exposures or multiple components of an exposure, and it is most relevant to consider intervening on them jointly. In addition, sometimes exposures are continuous, and one would like to have an easy-to-interpret causal effect. In this workshop, we will walk through how to define causal effects (what are called causal estimands) for categorical, continuous, and multiple exposures. Read more
Causal Inference in Networks: Applications to Public Health
May 11, 2026
8:30AM-12:30PM
Description:
This short course will provide a practical and conceptual tutorial on statistical methods for causal inference in the presence of networks, with motivating examples from public health. Standard causal inference approaches typically assume that one individual’s treatment or exposure has no effect on another individual’s outcome; however, this assumption is often violated when people are socially, spatially, or behaviorally connected. Such interference or spillover is common in studies of infectious disease transmission, harm reduction interventions, vaccination programs, educational initiatives, and community-level public health strategies, where the behavior or treatment of one person can affect others in their network. When these dependencies are ignored, researchers risk understating the true benefits or unintended consequences of interventions or failing to understand how effects propagate through a population. Read more
INSTRUCTORS:
Causal Machine Learning for Discovering Heterogeneous Treatment Effects
May 11, 2026
1:00PM-5:00PM
Description:
This short course provides a comprehensive overview of current state-of-the-art approaches for causal machine learning for the discovery of heterogeneous treatment effects. As precision medicine and personalized policy interventions become increasingly central to research and practice, understanding who benefits most from treatments is crucial for optimizing resource allocation and improving outcomes. Read more
Stress-Testing Assumptions: Bayesian Methods for Sensitivity Analysis in Causal Inference
May 11, 2026
1:00PM-5:00PM
Description:
Observational studies are often conducted to estimate causal effects of biomedical treatments. These methods invariably rely on statistical and causal identification assumptions. The former are required in settings with imperfectly observed data (e.g. measurement error, missing data, etc.) to connect the complete data distribution to the observed data distribution. The latter are required to connect the observed data distribution to the distribution of potential outcomes. In general, both sets of assumptions are untestable. Read more
