Graduate School of health and Medical Sciences

Targeted Minimum Loss-based Estimation (TMLE) for Causal Inference
Provider: Faculty of Health and Medical Sciences

Activity no.: 3340-23-00-00
	Enrollment deadline: 04/05/2023

Date and time	06.06.2023, at: 08:00 - 09.06.2023, at: 15:00
Regular seats	25
Course fee	3,360.00 kr.
Lecturers	Helene Charlotte Wiese Rytgaard
ECTS credits	2.80

Contact person	Susanne Kragskov Laupstad E-mail address: skl@sund.ku.dk

Enrolment Handling/Course Organiser	PhD administration E-mail address: phdkursus@sund.ku.dk

Aim and content This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrolment deadline, available seats will be allocated to the waiting list. The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member universities. All other participants must pay the course fee Learning objectives A student who has met the objectives of the course will be able to: 1. Explain the fundamental principles of statistical inference using targeted minimum loss-based estimation (TMLE) and its application as a general framework for estimation of causal effects. 2. Implement TMLE using R software to estimate average treatment effects and time-varying treatment effects based on simulated data, and assess the accuracy and efficiency of the estimators. 3. Compare the assumptions and performance of TMLE to related causal inference tools such as inverse probability weighting and standardization, and discuss the strengths and limitations of each approach. 4. Evaluate the suitability of super learning and its application in TMLE, and implement the algorithm to improve estimation accuracy. 5. Discuss and evaluate the challenges and opportunities in time-varying settings in causal inference, including time-varying treatments and time-dependent confounding, and how TMLE can be used to address these challenges. Content Targeted minimum loss-based estimation (TMLE) is a general framework for estimation of causal effects that combines semiparametric efficiency theory and machine learning in a two-step procedure. The main focus of the course is to understand the overall concept, the theory, and the application of TMLE. Topics covered include: • The roadmap of targeted learning. • Basics of causal inference, including counterfactual notation, hypothetical interventions, the g-formula, and the average treatment effect (ATE). • Causal effect estimation in nonparametric models: target parameters, nuisance parameters, efficient influence functions, asymptotic linearity, and statistical inference based on the efficient influence function. • TMLE as a two-step procedure involving initial estimation followed by a targeting step. • Super learning: combining multiple machine learning algorithms via loss-based cross-validation. • Extensions to more complex data settings: survival outcome, time-dependent confounding, dynamic treatment regimes. • Basic usage of existing software in R. Course material: A combination of research papers, textbook, and lecture notes. Participants The course is relevant for Ph.D.-students with sufficient background in mathematics and statistics. To participate in the practicals, the participants should have knowledge of the statistical software R. Max. 25 participants. Relevance to graduate programmes The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH: - All graduate programmes Language English Form Four days with lectures and practicals. Course director Helene Charlotte Wiese Rytgaard, Assistant Professor, Section of Biostatistics, Department of Public Health. Teachers Helene Charlotte Wiese Rytgaard, Thomas Alexander Gerds, Anders Munch Dates June 6-9, 2023; all days 8-15 Course location CSS Registration Please register before May 4 2023 Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrolment. Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

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