FENStatS News
New ECAS course is under way: next October in France. Now it will be devoted to causal inference.
In an era of rapidly advancing technologies and exponential data growth, many scientific and industrial challenges, such as drug discovery, public health, economic policy design, and social program evaluation, require robust methodologies to derive credible causal conclusions from both observational and experimental data. For example, a medical researcher might seek to assess the risk factors of a disease, an economist could analyze the impact of a job-training program on employment prospects, or a sociologist might study how divorce affects children’s educational outcomes.
ECAS and SFdS (the French Statistical Society) organize a course entitled
Causal inference: from foundations to applications
that will be held on October, 4-8, 2027, in La Villa Clythia, CAES du CNRS, Fréjus, France.
This ECAS-SFdS course focuses on discovering, identifying, and estimating causal relationships between a treatment (X) and the evolution of an outcome (Y), particularly when relying on observational data prone to selection biases and confounding factors. Building on Judea Pearl’s theory (DAGs and do-calculus) and Donald Rubin’s framework (Potential Outcomes), it introduces the fundamental principles and tools of causal inference, covering methodological, applied, computational, and mathematical aspects. This will enable participants to address real-world problems with rigor and precision.
While some experience in programming with R is a plus, it is not a prerequisite. The course will be taught in English, and no prior knowledge of causal inference is required - it will start with the basics.
More information about the deadlines, fees, accommodation and meals is available here.