GRA 6845 Causal Inference with Big Data (6 ECTS)

GRA 6845 Causal Inference with Big Data (6 ECTS)

Course code: 
GRA 6845
Course coordinator: 
Jon H Fiva
Course name in Norwegian: 
Causal Inference with Big Data (6 ECTS)
Product category: 
MSc in Business - Elective course
2017 Autumn
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

In the 21st century, information is created and stored at unprecedented rates. The access to high-dimensional large data sets – “Big Data” – has opened up new possibilities for business analytics and economic research. Massive datasets alone are, however, insufficient to answer fundamental questions within business and economics. Using the potential outcome framework, we explore various methods useful for causal inference in the Big Data era. We discuss the promise and pitfalls of large-scale experimentation, machine learning, and regression discontinuity designs. We consider empirical applications relevant for business and policy analysis.

Learning outcomes - Knowledge

After having completed this course, students should be familiar with the potential outcome framework and microeconometric methods useful for answering “what if” questions using Big Data. Students learn the distinction between causal models and predictive models.

Learning outcomes - Skills

Students learn how large data sets should be used for decision making. Students receive hands-on experience by replicating and extending published research papers.

Learning Outcome - Reflection

Students learn how to critically discuss empirical research of the Big Data era. They are trained to pay particular attention to the underlying assumptions of the methods used and the empirical implementation.

Course content

The course covers the following topics:

  • What is big data?
  • The potential outcome framework
  • Regression and matching
  • Large-scale experimentation
  • Treatment effect heterogeneity
  • False positives and p-hacking
  • Publication bias
  • Regression discontinuity designs
  • Supplementary analysis
  • Data visualization
Learning process and requirements to students

Students are expected to participate actively in class and will present some of the articles on the reading list.

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class that is not included on It's learning or text book.

Software tools

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Form of assessment:
Written submission
Exam code:
Grading scale:
Grading rules:
Internal and external examiner
Examination when next scheduled course
100No1 Semester(s)Group/Individual (1 - 3)
Exam category:Submission
Form of assessment:Written submission
Grouping (size):Group/Individual (1-3)
Duration:1 Semester(s)
Exam code:GRA68451
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam organisation: 
Ordinary examination
Total weight: 
Sum workload: 

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.