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
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.
Required prerequisite knowledge
Recommended previous knowledge
|Individual essay||1/1||Pass - Fail|
- 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).
Method of work
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 (2008). "Mostly Harmless Econometrics", Princeton University Press. Chapters 5.1 (Individual Fixed Effects) and 8.2 (Clustering and Serial Correlation in Panels)
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.
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.
Sist oppdatert: 22.01.2020