ONLINE COURSE
Causal Inference for Data Science and Analytics
Foundations, Assumptions and Applications
15
CLASSES
15
EXERCISES
5
QUIZZES
20
SCIENTIFIC ARTICLES
COURSE CERITIFCATE
COURSE OVERVIEW
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.
WHY IS CAUSAL INFERENCE USEFUL?
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.
WHAT IS THE FOCUS OF THIS COURSE?
This 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.
WHO IS THE COURSE FOR?
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.
3. Leaders and data entrepreneurs with keen interest in understanding the world of data-based decision-making.
Some prior knowledge of statistics is recommended.
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 seven 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 extra fee. Below is the reason for the extra 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 in finding 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 your Week 1 lessons, or before accessing material of Week 2 lessons, 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 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.
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The course starts on 11th of November 2025.
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