The purpose of the Center for Targeted Machine Learning and Causal Inference (CTML) is to leverage the unique developments in statistical machine learning methodology within the Group in Biostatistics towards adaptation of these methods in research and applications. The Targeted Learning approach provides a template to construct optimal machine learning algorithms for answering any type of (often causal) question about any type of observed data system, while providing formal statistical inference. Thus, the potential sphere of relevant applications is infinite, covering an enormous range of randomized trials, sequentially adaptive randomized trials, integrating randomized and observational data, federated learning, and complex longitudinal observational studies. Indeed, there exists strong interest in adaptation of these methods, including from the FDA, industry and researchers. However, the uptake of specific applications of the targeted learning methods (beyond prediction) has been slowed due to educational and computational barriers. Our efforts to attack this challenge have taken three overlapping paths:
1) development of new software tools (eg, in R) that can be both applied by end-users to analyze their data with relative ease (see https://tlverse.org/tlverse-handbook/)
2) to apply these methods in collaborative research, by working closely with groups to assist in analyzing their data
3) training for translation of these methods into practice (see for instance https://tlverse.org/acic2022-adv-workshop/).
Goals and Objectives
- Build a team of researchers from data sciences, statistics and health fields to advance and disseminate the state of the art in causality, statistics and machine learning.
- Develop open source software and tools accessible and available to public health researchers and statisticians in the field.
- Apply novel methods and software to illustrative case studies.
- Collaborate with institutions in private and public health, academics and regulators on key research projects.
- Convene leaders in statistics and data sciences to agree and maintain methods standards for Big Data analysis in health.