GRA 6845 Causal Inference with Big Data
GRA 6845 Causal Inference with Big Data
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.
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.
Students learn how large data sets should be used for decision making. Students receive hands-on experience by replicating and extending published research papers.
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.
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
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.
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.
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 |
|---|
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 |
| Activity | Duration | Comment |
|---|---|---|
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 |
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.