Causal Inference for Data Science and Analytics

Foundations, Assumptions and Applications

15 weeks










3-5h per week



Because most questions are causal in their nature, it is important for data analysts and data scientists to become familiar with the foundations of cause and effect studies. Causal inference is a theoretical and methodological field of study that offers tools and  techniques to analyse causal relationships.


Causal inference provides a methodological foundation for impact evaluations, marketing interventions, and evidence-based policy developments, to name a few. Causal inference helps us in revealing subtle differences about which things work better and it provides foundational knowledge for the design and analysis of experiments.


The course provides an introduction to causal inference in terms of foundations, causal reasoning, scientific design and data analysis. The focus of this course is to develop understanding about the key concepts that influence the design and analysis of data when analysing causal relationships.


Those who like to solve real life problems by using tools that solve questions which are causal in their nature. The following groups are welcome:

1.Data scientists and analysts, regardless of whether you hold one of these roles by profession or in spirit.

2. Applied researchers in the social, behavioural and medical sciences.

2. Leaders and data entrepreneurs with keen interest in understanding the world of data-based decision-making.

No prior knowledge of statistics is required, although those who have some, will be able to review it when learning about the impact that statistical thinking has on analysis of causal relationships (Week 2).


The material was previously used for lectures, seminars and workshops at different venues, including University of Helsinki, Sigmund Freud University, Uppsala University and Tsinghua University.


Dr. Ana Kolar holds a PhD in Statistics with expertise in Causal Inference. Her PhD advisor and Causal Inference guru is one of the greatest applied statisticians of today – Dr. Donald B. Rubin, Emeritus Professor from Harvard University. Read more about Ana here.


This online course covers material of five academic weeks of in-person lessons. Due to an online implementation of the course and the deep-learning teaching approach that we use, it takes about 5 months to complete this course.

The Learn&Apply module consists of extra material, so those enroled to Learn&Apply module should add another month of time to complete this course.

The course consists of video lessons, exercises and quizzes. For each week’s curriculum you will need about 3 weeks to complete. An assistance from the instructor is available for all the exercises. The Learn&Apply module includes also online consultation hours with the instructor.

Upon successful completion of all the course activities, you receive a course certificate.

Who should take advantage of the Learn&Apply module?
This module is useful for those who want to apply the knowledge with their own project. The module offers extra material in terms of exercises and assignments linked to your project. The purpose of this extra material is to engage you in working on your project alongside the course. With more exercises and assignments you receive also more assistance from the instructor.


By the end of this course you will develop the reasoning that is required in causal inference to design studies, analyse causal relationships and perform impact evaluations with experimental or observational data.

In the first week, we introduce the concept of causality as it is used in real life, philosophically and in data analysis. We explain the difference between physical and factual causes, and show the role that causal thinking has on the formulation of research questions, design and analysis of collected data.

7 videos, 2 exercises and 2 quizzes to complete.

During the second week we learn about the scientific design in causal inference and about the importance of statistical thinking. We look at how crucial statistical thinking is for objective scientific designs and what are the ways to develop it.

7 videos, 2 exercises and 2 quizzes to complete.

During the third week we look at the problem of bias and assumptions. We introduce different types of biases that can influence scientific design, analysis and conclusion making, and, we look at the impact that posing and justifying assumptions has on validity of obtained data insights.

7 videos, 2 exercises and 2 quizzes to complete.

During the fourth week, we talk about a required knowledge to develop and implement an effective data collection strategy which guides an analysis of data in a causal-effect fashion. By introducing the required knowledge, we show common misconceptions about causal inference and causal effect studies.

7 videos, 2 exercises and 2 quizzes to complete.

During our last, fifth week, we explain the difference between observational and experimental data, and show how to proceed with a causal-effect study when only observational data is available. We cover all the steps, from forming research questions, to posing assumptions and thinking of the ways to justify them.

We show how to think when designing such studies, as also how to think when analysing obtained data. We use real world examples from different impact evaluations that were used for further decision-making.

 7 videos, 2 exercises and 2 quizzes to complete.


You can access the course material for 12 months from the day of enrolment or the start of the course (whatever comes later). An extension of this timeline is possible, but it comes with extra fee. Below is the reason for the extra fee.

Our online students receive guided assistance from the course instructor. Whenever students submit an exercise, this exercise needs to be approved by the course instructor. In case students have difficulties with exercises, the course instructor helps in finding the way to solution. 

When you enrol in the course, certain amount of time is allocated to the course instructor’s calendar for interacting with you. That is also why each time this course is offered, only a limited number of e-seats is available. Our course instructor is only a human being, but she is an excellent teacher.

Yes, you can cancel the course anytime during your Week 1 lessons, or before accessing material of the Week 2 lessons, and you will get full refund. The cancellation needs to be done in writing by emailing to in 30 days after enrolment or official start of the course (whichever comes later). Cancellations after this period of time will not be possible.

The Online Consultation Hour With The Instructor is available to clarify questions during your learning process. When you enrol for the course, you get access to an online consultation booking calendar where you select the time that suits you. To get the most out of this online consultation hour, we recommend that you send questions for which you need clarification  at least 72 hours before the consultation with the instructor. Details of where to send questions are provided once you enrol for the course.

ATTENTION! The Online Consultation Hour With The Instructor is complementary for those enroled as Early Bird and those enroled to the Learn&Apply module of the course. Early Bird enrolment is offered three times per year. Check for the Early Bird offer below, or leave your email in the “Stay Tuned” section to be informed about the upcoming courses.

Is it possible to purchase consultation hours with the instructor during the course? Yes, but it is subject to availability. If your enrolment already includes a consultation hour with the instructor like with Early Bird and Lear&Apply enrolment, then you can purchase a maximum three extra consultation hours at 125€ per hour. If you are enroled in the Learn Independently module you can also purchase a maximum three extra consultation hours at 125€ per hour. If additional consultations with the instructor are still needed, further options are available.

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The course starts on 1st of August 2024.

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Join us and elevate your knowledge by learning how to analyse causal relationships in scientifically objective way.

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