GRA 6845 Causal Inference with Big Data

GRA 6845 Causal Inference with Big Data

Course code: 
GRA 6845
Department: 
Economics
Credits: 
6
Course coordinator: 
Jon H Fiva
Product category: 
Master
Portfolio: 
MSc in Business Analytics
Semester: 
2019 Autumn
Active status: 
Active
Teaching language: 
English
Course type: 
One semester
Introduction

In the 21st century, information is created, digitized 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 and 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.

General Competence

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
  • Feature engineering and feature learning
  • Introduction to image analysis
  • Introduction to text analysis
Teaching and learning activities

Students are expected to participate actively in class and must present some of the articles on the reading list. Student presentations count for 20% of the grade.

 

Software tools
R
Stata
Additional information

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.

All parts of the assessment must be passed in order to get a grade in the course.

 

Qualifications

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:
Submission
Form of assessment:
Written submission
Exam code:
GRA68452
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
80No1 Semester(s)Group/Individual (1 - 3)Term paper
Exam category:
Activity
Form of assessment:
Presentation
Exam code:
GRA68453
Grading scale:
ECTS
Grading rules:
Internal examiner
Resit:
Examination when next scheduled course
20No -Group (1 - 4)Presentation
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:80
Invigilation:No
Grouping (size):Group/Individual (1-3)
Duration:1 Semester(s)
Comment:Term paper
Exam code: GRA68452
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam category:Activity
Form of assessment:Presentation
Weight:20
Invigilation:No
Grouping (size):Group (1-4)
Duration: -
Comment:Presentation
Exam code: GRA68453
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
100