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Causal Inference and Propensity Score Methods

Causal Inference and Propensity Score Methods

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Join us for an 4-day course designed to delve into the the world of epidemiologic research, a field that applies statistical methods to uncover treatment effects and causal relationships from patient data.

What do you gain from this programme?

Learn how to estimate treatment effects from patient data using advanced statistical tools. This 4-day course introduces the principles of causal inference in epidemiology, from randomized controlled trials to observational studies. Participants gain hands-on experience in regression, propensity score methods, and target trial design using R, developing the skills to critically assess and apply these techniques in real-world medical research.

Please scroll down to read the detailed daily course curriculum of Causal Inference and Propensity Score Methods.

Assistent Professor: Thomas Klausch

Thomas Klausch (PhD in Survey Methodology and Statistics from Utrecht University, 2014) is an Assistant Professor at the Department of Epidemiology and Data Science, Amsterdam University Medical Center. He is especially interested in methods for personalized medicine and prevention, including models for estimating treatment effect heterogeneity from observational data, optimal treatment regimens, and personalized (cancer) screening. He has worked with frequentist and Bayesian approaches, statistical Machine Learning, and traditional statistical models.

During his PhD and postdoc, he worked at the statistics departments of Utrecht University and Statistics Netherlands (CBS). His research focused on estimating and adjusting errors in statistics based on population surveys, such as non-response (missing data) and measurement error.

Hereby the curriculum per day:

  • Topics day 1: Causal theory and basic estimators
    • Neyman-Rubin causal model (RCM) and potential outcomes
    • Definitions of causal effects
    • Causal effects vs. associational effects
    • Randomized experiments and conditionally randomized experiments
    • Exchangeability and conditional exchangeability
    • Standardization estimator
    • Inverse probability estimator
    • Study groups (afternoon)

  • Topics day 2: Regression estimation and propensity score weighting
    • Observational designs
    • Identifying assumptions to estimate causal effects from observational designs
    • Regression estimation to estimate causal effects
    • Weaknesses of regression estimation for estimating causal effects
    • Propensity score theory
    • Estimating propensity scores
    • Assessing overlap and balance using the propensity score
    • Inverse propensity score weighting
    • Computer tutorials in R (afternoon)

  • Topics day 3: Propensity score stratification and matching
    • Propensity score stratification
    • Marginal mean weighting through stratification
    • Conditional treatment effects
    • Simple nearest neighbor propensity score matching
    • Computer tutorial in R (afternoon)

  • Topics day 4: Target trial emulation and combination of estimation methods
    • Design of observational studies
    • The target trial framework
    • Extensions of nearest neighbor matching
    • Advanced matching methods
    • Combination of regression adjustment, matching, and stratification
    • Double robust estimation methods
    • Study groups and computer tutorial in R (afternoon)

Still have questions? Feel free to contact:

Dr Gerdien van Eersel, education coordinator

Monday and Thursday: +31 20 566 6691

Tuesday and Wednesday: +31 6 29 131 812

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