CE_17C: Targeted Learning in the tlverse: Techniques and Tools for Causal Machine Learning (Added Fee)

Great care is required when disentangling intricate relationships for causal and statistical inference in medicine, public health, marketing, political science, and myriad other fields. However, traditional statistical practice ignores complexities that exist in real-world problems, for example, by avoiding interaction terms in regression analysis because such terms complicate and obfuscate the interpretation of results. The field of Targeted Learning (TL) presents a solution to such practices by outlining a modern statistical framework that unifies semiparametric theory, machine learning, and causal inference. This workshop provides a comprehensive introduction to TL and its accompanying free and open source software ecosystem, the tlverse (https://github.com/tlverse). It will be of interest to statisticians and data scientists who wish to apply cutting-edge statistical and causal inference approaches to rigorously formalize and answer substantive scientific questions. This workshop incorporates discussion and hands-on R programming exercises, allowing participants to familiarize themselves with methodology and tools that translate to improvements in real-world data analytic practice. Participants are highly recommended to have had prior training in basic statistical concepts, such as confounding, probability distributions, (linear and logistic) regression, hypothesis testing and confidence intervals. Advanced knowledge of mathematical statistics may be useful but is not necessary. Familiarity with the R programming language is essential.