ONLINE COURSE
Causal Inference for Data Science and Analytics
Foundations, Assumptions and Applications
15
CLASSES
15
EXERCISES
5
QUIZZES
20
SCIENTIFIC ARTICLES
COURSE CERTIFICATE
COURSE OVERVIEW
Because most questions are causal in their nature, it is important for data analysts, data scientists and researchers in general, to become familiar with 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.
WHY IS CAUSAL INFERENCE USEFUL?
Causal inference helps us in revealing subtle differences about which things work better. It provides methodological foundation for impact evaluations, randomised-controlled trials, marketing interventions, and evidence-based policy developments, to name a few. It is a foundational knowledge for design and analysis of experiments.
WHAT IS THE FOCUS OF THIS COURSE?
This course offers a holistic introduction to causal inference – from first principles to foundations, from scientific design to causal reasoning. It equips you with understandings on what it takes to analyse causal relationships in scientifically objective way. It provides you with tools and techniques on how to do it.
During this course, you develop understanding about the key concepts that influence a causal design, which is the scientific design for analysing causal relationships. You learn about the science behind causal thinking, which is the key ingredient for development of causal design and objective conclusion-making.
WHO IS THE COURSE FOR?
For those who like to solve real life problems by using tools that solve questions which are causal in their nature. And, for those interested in higher-order thinking. The following groups are welcome:
1.Data scientists, data analysts and computer scientists, regardless of whether you hold one of these roles by profession or in spirit.
2. Applied researchers, e.g., in the social, behavioural, medical, environmental/life sciences, law and business.
3. Leaders and data entrepreneurs with keen interest in understanding the world of cause-and-effect conclusion-making and data-based decision-making.
Some prior knowledge of statistics is recommended, for example, understanding regression analysis and inferential statistics.
ABOUT COURSE MATERIAL
The material was previously used for lectures, seminars and workshops at different venues, including Sigmund Freud University, Uppsala University and Tsinghua University. Currently, a similar version of this course is available to students at University of Helsinki. Find future implementations for master’s students here and for doctoral students here and here.
ABOUT INSTRUCTOR
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.
ABOUT THE FLOW oF tHE cOURSE
This online course covers material of eight academic weeks of in-person lessons. Due to the deep-learning teaching approach, it takes about 2-5 months to complete this course.
Upon successful completion of all the course activities, you receive a course certificate.
COURSE OUTLINE
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.
During the second week we learn about 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.
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.
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.
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 causal-effect studies, as also how to think when analysing causal relationships. We use real world examples from different impact evaluations that were used for evidence-based decision-making.
COMMON QUESTIONS
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 a fee. Below is the reason for the the fee.
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 to find a way to a solution.
When you enroll in the course, certain amount of time is allocated to the course instructor’s calendar for interacting with you. This is why only a limited number of e-seats is available.
Yes, you can cancel the course anytime during the first two classes, or before accessing material of Class 3, and you will get full refund. The cancellation needs to be done in writing by emailing to admin@tarastats.com in 30 days after enrolment or official start of the course (whichever comes later). Cancellations after this period of time are not be possible.
The Online Session with the course instructor is available to clarify questions during your learning process. When you enrol for the course, you get access to an online session calendar where you select the time that suits you. To get the most out of this online sessions, we recommend that you send questions for which you need clarification at least 72 hours before the session with the instructor. Details of where to send questions are provided once you enrol for the course. These sessions are available after completing Class 4 learning activities. If needed, extra sessions can be purchased. In general it is recommended to take this sessions at the end of the course, i.e., once all the classes and final assignment is completed.
What people say
Join us to learn about the hottest topic in today´s data world!
Are you ready to enrol?
Ready to develop higher-order thinking by learning how to analyse causal relationships in scientifically objective way?
The course starts on 15th of November 2025.
Online Course
Causal Inference for Data Science and Analytics- 15 Classes
- 20 Scientific articles
- 15 Exercises & 5 Quizzes
- Frequent feedback by course instructor
- Final Assignment to deepen understandings
- 2x Online session with course instructor (1h30min each)
- COURSE CERTIFICATE
The fee is inclusive of 25.5% VAT.
This is a fully self-paced course that includes frequent interaction with the course instructor.
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