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Causal Inference I


Admission requirements

Basic knowledge of R and generalized linear models.


A key goal of data science is to help people make better decisions. For example, in health settings, the goal is to help patients and doctors decide among several possible strategies. However, one cannot learn what the best decisions are from data alone. In order know which decisions to make, we need to be able to distinguish cause from effect. This is only possible if we combine data with causal expert knowledge. By combining data with knowledge on how data were collected (i.e., design) and with known mechanisms that play a role in the collected data (i.e., the causal structure), we can use data to answer causal questions. In this course we consider how to formulate causal questions, how to design studies to answer causal questions and how to draw causal inference from data taking into account sources of bias which may arise.

Course Objectives

After successful completion students are able to:

  1. Explain the differences between causal and non-causal analysis of data and recognize questions and situations that ask for a causal approach to data analysis;
  2. Formulate a causal question of interest using the potential outcomes framework;
  3. Recognize the difference between experimental and observational designs and several subtypes of the latter and choose the optimal design for a given situation;
  4. Identify the pitfalls of causal analysis, such as confounding, missing data, selection bias and measurement error;
  5. List the causal assumptions that are needed to identify a causal effect and visualize such assumptions using directed acyclic graphs;
  6. Assess whether causal assumptions hold in a given example application and reason about the expected direction of bias in case the assumptions do not hold;
  7. Apply several modern computational approaches in causal data analysis that aim to counter the pitfalls: outcome regression, inverse probability weighting, multiple imputation and attenuation factors for measurement error;
  8. Identify which of the discussed computational methods in this course give valid causal inference in a new practical situation, apply the methods and draw valid conclusions;
  9. Effectively explain a causal data analysis to a multidisciplinary data science team, including motivation and evaluation of the causal assumptions and interpretation of results.


You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.
MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).
For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.

Mode of Instruction

Lectures, exercises and assignments.

Assessment method

This course has an individual exam, mandatory assignments and a group assignment at the end of the course. The final grade will be determined as a weighted average of the group assignment (30%) and the (retake) exam (70%). Both the grade of the assignment and the exam should be at least 5.5. If the grade of the assignment is lower than 5.5, the assignment can be improved, but the final grade of the assignment cannot become higher than 5.5.

Partial grades cannot be carried over to the next academic year, the grade of the group assignment and the grade of the exam should be obtained within the same year.

Reading list

The list of literature will be posted on Brightspace.


It is the responsibility of every student to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.

Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.
Extensive FAQ's on MyStudymap can be found here.


We will communicate with students using Brightspace.