The course introduces causal analysis and teaches PhD students how to apply these methods to actual observational and experimental data. The goals of this course are to equip PhD students with the intuition and skills necessary to understand and estimate causal effects and to enable students to use these methods. Relevant topics and data sets for research projects as well as potential uses in research and policy analysis will be discussed. Examples from the literature and step-by-step tutorials offer hands-on experiences in utilizing the methods.
At the end of the course PhD students
- are able to critically assess reports discussing associations between variables and interpret causal effects
- are able to independently estimate causal effects
- understand the assumptions necessary to estimate causal effects
- know how to write and run do-files with relevant commands and produce tables and figures in STATA
This course will contribute to students' general competence in
- academic writing
- econometric analysis with testing of hypotheses on numerical data using relevant software
- search and review of relevant literature
- presentation and academic discussion
- Short review of basic regression techniques
- Causal inference using potential outcomes
- Randomized experiments
- Regression and causality
- Instrumental variables (LATE)
- Fixed effects and panel data
- Regression discontinuity design
- Synthetic control methods
Required prerequisite knowledge
Recommended previous knowledge
|Individual essay||1/1||Pass - Fail|
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: 17.06.2019