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Causal Inference, Certificate

  • Application Deadline
  • 5 days
University rank #70 (WUR) ,
This course combines counterfactual theory and graph theory to present an integrated framework for causal inference from observational data, with a special emphasis on complex longitudinal data. The course presents the latest methodologic developments for the design and analysis of longitudinal studies. 


The goal of many epidemiologic studies is to quantify the causal effect of a treatment (or exposure) on an outcome. In contrast, commonly used statistical methods provide measures of association that may lack a causal interpretation even when the investigator adjusts for all potential confounders in the analysis of a properly designed study.

To eliminate the discordance between the causal goals and the associational methods in epidemiology, it is necessary to a) formally define causal concepts such as causal effect and confounding, b) identify the conditions required to estimate causal effects, and c) use analytical methods that, under those conditions, provide estimates that can be endowed with a causal interpretation. These so-called g-methods can be used under less restrictive conditions than traditional statistical methods. For example, g-methods allow one to estimate the causal effect of a time-varying treatment in the presence of time-varying confounders that are affected by the treatment.

Detailed Programme Facts

  • Programme intensity Full-time
    • Full-time duration 5 days
  • Credits
    1 ECTS
    1.4 ECTS
  • Languages
    • English
  • Delivery mode
    On Campus

Programme Structure


  • Recognize and formulate well defined questions concerning causal effects
  • Use causal diagrams to represent a priori subject-matter knowledge and assumptions
  • Identify the settings in which conventional methods for data analysis are inadequate
  • Use provided software to estimate causal effects using g-methods.


Prof. Miguel Hernán, MD and Sonja Swanson, ScD

English Language Requirements

This programme requires students to demonstrate proficiency in English.

Academic Requirements

The course is intended for health researchers or other data scientists who will use observational studies to estimate causal effects as part of their current or future professional career. Examples include: epidemiologists, (bio-)statisticians, and other clinical or public health researchers.

Tuition Fee

  • International Applies to you

    1190 EUR/full
    Tuition Fee
    Based on the original amount of 1190 EUR for the full programme and a duration of 5 days.
  • EU/EEA Applies to you

    1190 EUR/full
    Tuition Fee
    Based on the original amount of 1190 EUR for the full programme and a duration of 5 days.
We've labeled the tuition fee that applies to you because we think you are from and prefer over other currencies.

Living costs for Rotterdam

  • 760 - 1270 EUR/month
    Living Costs

The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.


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