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 will be given five assignments during the semester, focusing on hands-on application of the material covered in class.
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 should normally be given in class, but there will also be possibilities to use video communication when necessary. Slide decks from the presentation will be included in the 40% element that should be handed in towards the end of the course. This submission will also include other assigned tasks from the semester (e.g., discussion notes).
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.
Continuous assessment will no longer exist as an examination form from autumn 2023. For questions regarding previous results, contact InfoHub.
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.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 40 Grouping: Group/Individual (1 - 5) Duration: 1 Semester(s) Comment: This submission is composed of different assigned tasks from the semester. Information will be given during the class. Exam code: GRA 68455 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Submission Form of assessment: Written submission Weight: 60 Grouping: Group/Individual (1 - 5) Duration: 30 Day(s) Comment: Term paper. Exam code: GRA 68456 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this 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.