This is the study programme for 2019/2020. It is subject to change.

In most of economics, marketing and business management, we are interested in causal relations between variables, rather than mere correlations. For example, it is not the correlation between marketing expenses and sales that is of interest, but the effect of increasing marketing expenses for a product on the sale volume of the same product.

Learning outcome

Students should be conversant about methods for estimating and identifying causal effects, and have the intuition and skills necessary to design and implement empirical strategies for causal analysis.

Students should be able to
  • Understand causal modeling
  • Gain the statistical tools for causal analysis
  • Design and implement empirical strategies for causal analysis
  • Become aware of underlying assumptions, strengths, and weaknesses of different experimental and quasi experimental approaches
  • Become acquainted with key players in the development and implementation of strategies for causal analysis

General competence
The student should be conversant in the world of causal analyses, using instrumental variables, difference-in-difference, regression discontinuity design, synthetic control methods, and randomized controlled trials. They should be able to be consumers and producers of causal analyses. They should have the background to develop and implement ethically sound and scientifically valid empirical strategies. They should also have proficiency in software designed to implement causal analysis.


In this course, we study methods for estimating and identifying such causal effects. First, we discuss randomized experiments as the predominant way for establishing causality. Second, we discuss designs and methods for causal analysis using observational data. In particular, designs and methods covered include instrumental variables, fixed effects and panel data, difference-in-difference, regression discontinuity design, and synthetic control methods.

Required prerequisite knowledge

The course is open to all PhD students who have taken courses in statistics or econometric which include regression analyses.

Recommended previous knowledge

General PhD statistics courses including regression analysis or econometrics courses would be an advantage.


Weight Duration Marks Aid
Individual essay1/1 Pass - Fail
Student evaluation will be conducted according to the regulations set forth by the Faculty of Social sciences.

Coursework requirements

  • Full participation during the whole course is required. (One week on intensive lectures and PC-lab exercises).
  • Active participation in discussions in general.
  • In addition, there is a term paper requirement ( 8-10 pages, demonstrating knowledge and application skills).

Course teacher(s)

Course teacher
Yuko Onozaka , Mari Rege
Course coordinator
Ragnar Tveterås

Method of work

The course will be taught through a combination of lectures, seminars and exercises. The seminars will include student presentations and discussions. Each student will be required to give one seminar presentation. All students are expected to read required literature ahead of the seminars and to participate actively in the discussions. Exercises will be undertaken on empirical cases (firms, sectors, markets) using appropriate data and software tools. Students will also have a written assignment in addition to the final essay.

Estimated student workload in hours:
1. Lecture 28
2. Seminar/lab exercise 8
3. Specific supervision 20
4. Student's self studies 50
5. Written assignment 40


Angrist and Pischke (2014). "Mastering Metrics - the Path from Cause to Effect", Princeton University Press.
Angrist and Pischke (2008). "Mostly Harmless Econometrics", Princeton University Press. Chapters 5.1 (Individual Fixed Effects) and 8.2 (Clustering and Serial Correlation in Panels)

Randomized Trials
Amy Finkelstein, Sarah Taubman, Bill Wright, Mira Bernstein, Jonathan Gruber, Joseph P. Newhouse, Heidi Allen, Katherine Baicker, and Oregon Health Study Group (2012): "The Oregon Health Insurance Experiment: Evidence from the First Year," The Quarterly Journal of Economics, 127 (3), pp. 1057-1106.
Jens Ludwig, Greg J. Duncan, Lisa A. Gennetian, Lawrence F. Katz, Ronagld C. Kessler, Jeffrey R. Kling, and Lisa Sanbonmatsu (2013): "Long-Term Neighborhood Effects on Low-Income Families: Evidence from Moving to Opportunity," American Economic Review Papers and Proceedings 103(3), pp. 226-231.

Instrumental Variable
Joshua Angrist (2006): "Instrumental variables methods in experimental criminological research: what, why and how," Journal of Experimental Criminology, 2, pp. 23-44.
Manudeep Bhuller, Gordon B. Dahl, Katrine V. Løken, Magne Mogstad (2016): «Incarceration, Recidivism and Employment" NBER Working Paper No. 22648.

Fixed Effects and Panel Data
Edward L. Glaeser and David C. Maré (2001): "Cities and Skills" Journal of Labor Economics, 19(2), pp. 316-342.

Martha J. Bailey and Andrew Goodman-Bacon (2015): "The War on Poverty's Experiment in Public Medicine: Community Health Centers and the Mortality of Older Americans," American Economic Review, 105(3), pp. 1067-1104.
Espen Bratberg and Karin Monstad (2015): "Worried sick? Worker responses to a financial shock," 33, pp. 111-120
David Card and Alan B. Krueger (1994): "Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania," American Economic Review, 84(4), pp. 772-93.

Regression Discontinuity Design
Pedro Carneiro, Katrine V. Løken and Kjell G. Salvanes (2015): "A Flying Start? Maternity Leave Benefits and Long-Run Outcomes of Children," Journal of Political Economy, 123(2), pp. 365-412.
Christopher Carpenter and Carlos Dobkin (2009): "The Effect of Alcohol Consumption on Mortality: Regression Discontinuity Evidence from the Minimum Drinking Age," American Economic Journal: Applied Economics, 1(1), pp. 164-182.

Synthetic Control Methods
Alberto Abadie and Javier Gardeazabal (2003): "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, 93(1), pp. 113-132.

Serial Correlation and Clustering
A. Colin Cameron and Douglas L. Miller (2015): "A Practitioner's Guide to Cluster-Robust Inference," Journal of Human Resources, 50, pp. 317-372.
Additional literature will be announced at the start of the course.

This is the study programme for 2019/2020. It is subject to change.

Sist oppdatert: 22.01.2020