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
Course name in Norwegian: 
Causal Inference with Big Data
Product category: 
Master
Portfolio: 
MSc in Business Analytics
Semester: 
2021 Autumn
Active status: 
Active
Level of study: 
Master
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
  • Large-scale experimentation
  • Noncompliance
  • Treatment effect heterogeneity
  • False positives, p-hacking and publication bias
  • Regression discontinuity designs
  • Supplementary analysis
  • High-dimensional data
Teaching and learning activities

The course consists of a combination of video lectures, Q&A sessions, student presentations, and assignments.

Students are strongly encouraged to attend on-campus sessions and participate actively in class. Students must present some of the articles on the reading list. These presentations count for 20% of the grade and should normally be given in class, but there will also be possibilities to use video communication when necessary.

Students will be given four assignments, focusing on hands-on application of the material covered in class and on software. Each assignment count 5% of the final 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.

This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course start.

At re-sit all exam components must, as a main rule, be retaken during next scheduled 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 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.

Covid-19 

Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.

Teaching 

Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.

Assessments
Assessments
Exam category: 
Submission
Form of assessment: 
Written submission
Weight: 
5
Grouping: 
Group/Individual (1 - 4)
Duration: 
1 Week(s)
Comment: 
Assignment 1
Exam code: 
GRA68454
Grading scale: 
Point scale leading to ECTS letter grade
Resit: 
All components must, as a main rule, be retaken during next scheduled course
Exam category: 
Submission
Form of assessment: 
Written submission
Weight: 
5
Grouping: 
Group/Individual (1 - 4)
Duration: 
1 Week(s)
Comment: 
Assignment 2
Exam code: 
GRA68454
Grading scale: 
Point scale leading to ECTS letter grade
Resit: 
All components must, as a main rule, be retaken during next scheduled course
Exam category: 
Submission
Form of assessment: 
Written submission
Weight: 
5
Grouping: 
Group/Individual (1 - 4)
Duration: 
1 Week(s)
Comment: 
Assignment 3
Exam code: 
GRA68454
Grading scale: 
Point scale leading to ECTS letter grade
Resit: 
All components must, as a main rule, be retaken during next scheduled course
Exam category: 
Submission
Form of assessment: 
Written submission
Weight: 
5
Grouping: 
Group/Individual (1 - 4)
Duration: 
1 Week(s)
Comment: 
Assignment 4
Exam code: 
GRA68454
Grading scale: 
Point scale leading to ECTS letter grade
Resit: 
All components must, as a main rule, be retaken during next scheduled course
Exam category: 
Activity
Form of assessment: 
Presentation
Weight: 
20
Grouping: 
Group (2 - 6)
Duration: 
30 Minute(s)
Comment: 
Presentation
Exam code: 
GRA68454
Grading scale: 
Point scale leading to ECTS letter grade
Resit: 
All components must, as a main rule, be retaken during next scheduled course
Exam category: 
Submission
Form of assessment: 
Written submission
Weight: 
60
Grouping: 
Group/Individual (1 - 4)
Duration: 
1 Semester(s)
Comment: 
Term paper
Exam code: 
GRA68454
Grading scale: 
Point scale leading to ECTS letter grade
Resit: 
All components must, as a main rule, be retaken during next scheduled course
Type of Assessment: 
Continuous assessment
Grading scale: 
ECTS
Total weight: 
100
Student workload
ActivityDurationComment
Examination
50 Hour(s)
Teaching
36 Hour(s)
Prepare for teaching
15 Hour(s)
Group work / Assignments
50 Hour(s)
Student's own work with learning resources
15 Hour(s)
Video lectures
Sum workload: 
166

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.