GRA 6840 Causal Inference with Big Data

Responsible for the course
Jon H Fiva

Department of Economics

According to study plan

ECTS Credits

Language of instruction

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 microeconomic research. Massive datasets alone are, however, insufficient to answer fundamental questions within business and politics. 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, and empirical applications relevant for business and policy analysis.

Learning outcome
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 are equipped with the intuition and skills necessary to understand and estimate causal effects for instance as a part of a master thesis or in future professional careers.


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 spesific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Compulsory reading
G. W. Imbens and D. B. Rubin. 2015. Causal Inference for Statistics, Social and Biomedical Sciences. Cambridge University Press
J.D. Angrist and J.S. Pischke. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press

Compendium of articles

Recommended reading

Course outline
The course covers the following topics:
* What is big data?
* The potential outcome framework
* Large-scale experimentation
* Experiments as instruments
* Treatment effect heterogeneity
* False positives and p-hacking
* Regression and matching
* Regression discontinuity designs

Computer-based tools

Learning process and workload
A course of 3 ECTS credits corresponds to a workload of 80-90 hours.

Students are expected to participate actively in class.

Term paper (individually or in groups for up to three students)

Form of assessmentWeightGroup size
Term paper100%Optional (individual or group of max 3 students)

Specific information regarding student assessment will be provided in class. This information may be relevant to requirements for term papers or other hand-ins, and/or where class participation can be one of several components of the overall assessment. Candidates may be called in for an oral hearing as a verification/control of written assignments.

Examination code(s)
GRA 68401 term paper accounts for 100 % of the final grade in the course GRA 6840.

Examination support materials
Not applicable
Permitted examination support materials for written examinations are detailed under examination information in the student portal @bi. The section on support materials and the use of calculators and dictionaries should be paid special attention to.

Re-sit examination
It is only possible to retake an examination when the course is next taught. The assessment in some courses is based on more than one exam code. Where this is the case, you may retake only the assessed components of one of these exam codes. All retaken examinations will incur an additional fee. Please note that you need to retake the latest version of the course with updated course literature and assessment. Please make sure that you have familiarised yourself with the latest course description.

Additional information
Honour code. Academic honesty and trust are important to all of us as individuals, and are values that are integral to BI's honour code system. Students are responsible for familiarising themselves with the honour code system, to which the faculty is deeply committed. Any violation of the honour code will be dealt with in accordance with BI’s procedures for academic misconduct. Issues of academic integrity are taken seriously by everyone associated with the programmes at BI and are at the heart of the honour code. If you have any questions about your responsibilities under the honour code, please ask. The learning platform itslearning is used in the teaching of all courses at BI. All students are expected to make use of itslearning.