SCI Shortcourses will take place at the 2027 American Causal Inference Conference (ACIC), held in Pittsburgh, PA, on Monday, February 8, 2027.
Additionally, 4 virtual shortcourses will be held January 19 – January 29, 2027.
IN-PERSON RATES
Student Member – $80
Regular Member – $110
Non-Member – $210
VIRTUAL RATES
Student Member – $40
Regular Member – $55
Non-Member – $105
Let’s Get Personal: Defining and Estimating the Effects of Dynamic Treatment Strategies Using Real-World Data
IN-PERSON
February 8, 2027
8:30AM-12:30PM
Description:
Questions about dynamic (i.e., ‘personalized’) treatment strategies that account for evolving individual characteristics increasingly outnumber the randomized trials available to answer them. When trials do not exist, real-world data, e.g., electronic health records and health insurance claims, can be used to estimate the observational analogues of the per-protocol effects of dynamic strategies, though, sufficient data and appropriate methods are required.
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Machine learning & nonparametric efficiency in causal inference
IN-PERSON
February 8, 2027
8:30AM-12:30PM
Description:
This short course covers the basics of efficient nonparametric estimation in causal inference, including estimating equations, TMLE, and double machine learning. It considers nonparametric efficiency bounds for causal estimands, and efficient bias-corrected estimators based on influence functions. Importantly, these estimators yield fast rates of convergence and normal limiting distributions, even in complex nonparametric models where nuisance functions (e.g., propensity scores) are estimated with modern machine learning tools. The estimators are often doubly robust. Background in mathematical statistics is useful but not required. The workshop covers both theory and application, including R code for implementing various methods.
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From Observational Data to Causal Graphs: A Practical Introduction to Causal Discovery
IN-PERSON
February 8, 2027
8:30AM-12:30PM
Description:
Causal inference requires partial knowledge of a system’s mechanisms—typically a causal graph. Commonsense or expert knowledge sometimes supplies it. But where both are sparse, misspecification looms and effect estimates go wrong. Capable of inferring causal structure from real-world observational data, causal discovery offers a remedy. In this short course, we introduce the mathematical foundations and key algorithms of causal discovery, engage with skepticism, and address practical gaps that block adoption of these methods to solve real-world problems.
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An Introduction to Single-World Intervention Graphs with Applications to Identification and Mediation
IN-PERSON
February 8, 2027
1:00PM-5:00PM
Description:
Causal models based on potential outcomes, also known as counterfactuals, were introduced by Neyman (1923) and extended to observational settings by Rubin (1974). Causal Directed Acyclic Graphs (DAGs) are another framework, originally introduced by Wright (1921), but subsequently significantly generalized and extended by Spirtes et al. (1993), Pearl (1995), and Dawid (2002), among others.
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Causal Machine Learning for Discovering Heterogeneous Treatment Effects
IN-PERSON
February 8, 2027
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.
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Beyond the Average Treatment Effect: Estimating the causal effects of binary, categorical, continuous, time-varying, and multivariate exposures in R
IN-PERSON
February 8, 2027
1:00PM-5:00PM
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, sometimes, exposures exist as a set, 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 short course, we will walk through how to define causal effects for categorical, continuous, and multiple exposures.
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Nicholas Williams
Regression Discontinuity Designs in Healthcare: Recent Advances and Challenges
VIRTUAL
January 19, 2027
12:00PM-4:00PM Eastern Time
Description:
The regression discontinuity design (RDD) is a quasi-experimental design that can be used to measure the causal effect of treatments that are assigned based on a running variable crossing a threshold. In healthcare, RDDs have grown increasingly popular but face key challenges, including discrete running variables, precise cohort definition, and time-to-event outcomes. We provide an overview of existing approaches to regression discontinuity and discuss methods to address each challenge.
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Mathematical foundations of causality
VIRTUAL
January 26, 2027
12:00PM-4:00PM Eastern Time
Description:
The purpose of the course is to give a good overview on mathematical foundations of causality, as well as directions of future research. Subject of research on causality and causal inference is at the intersection of mathematics, statistics, econometrics, social and computer science, and is currently a popular research topic.
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An Introduction to Filtered Probability Spaces with Applications to Causal Inference
VIRTUAL
January 28, 2027
12:00PM-4:00PM Eastern Time
Description:
In measure-based probability theory, there is a concept of a filtered probability space. A probability space consists of a sample space, an event space, and a probability measure. An event space is a sigma-algebra, meaning that the whole sample space is an event and also that the event space is closed under complements and countable unions. A filtration is an ordered family of sigma-algebras, and a filtered probability space is a probability space with a filtration.
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Pål Ryalen
Expanding the Causal Inference Toolkit: Using Mixed Methods to Strengthen Causal Studies
VIRTUAL
January 29, 2027
12:00PM-4:00PM Eastern Time
Description:
Mixed method approaches, which carefully integrate qualitative and quantitative data, are widely used in social sciences and public health. Yet their potential to strengthen causal inference remains underexplored. This course introduces mixed methods as an innovative and practical extension of the causal inference toolkit.
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