Causal Inference
PhD Course
Causal Inference in Statistics, Social, and Biomedical Sciences and Economics
Block course: September 2016 (4 days), September 20th – September 23rd
Time: 9am – 5pm, room tba
Course instructor: Professor Martin Spindler (UHH)
Course value: 2 SWS or 4 LP
Course overview:
The main goal of this course is to give an introduction to statistical methods for causal re-search with applications to Economics and Business Administration. PhD students working empirically are invited to attend the class. Every participant is expected to give a short presen-tation and / or to hand in a paper. Details will follow. It is highly welcome if you present own papers / ideas (if they t to the topics) or present empirical applications which are of interest for your research. Proposals are appreciated.
Topics to be covered and further reading (preliminary)
1. Rubin Causal Framework
- Little, R. and Rubin, D.B. (2000). Causal Effcets in Clinical and Epidemiological Studies Via Potential Outcomes: Conecpts and Analytical Approaches. Annu. Rev. Public Health 21, 121-145.
- Holland, P.W. (1986). Statistics and Causal Inference. Journal of the American Statisti-cal Association 81:945-969.
- *Rubin, D. B. (1974). Estimating Causal Effects of Treatments in Randomized and Non-randomized Studies. Journal of Educational Psychology 66: 5, 688-701.
2. Instrumental Variable Estimation I
- Imbens, G. W. (2014). Instrumental Variables: An Econometricians Perspective. Statisti-cal Science 29:3,323-358.
- And discussion in this issue of Statistical Science
3. Instrumental Variable Estimation II
- * Angrist, J. D. and Imbens, G. W. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica 62:2, 467-475.
- Angrist, J. D., Imbens, G. W. and Rubin, D. B. (1996). Identification of Causal Effects Us-ing Instrumental Variables. Journal of the American Statistical Association 91: 434, 444-455. Seite 2/2
4. Regression Discontinuity
- Imbens, G. W. and Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics 142:2, 615-635.
- Lee, D. S. and Lemieux, T. (2008). Regression Discontinuity Designs in Economics. Jour-nal of Economic Literature 48:2, 281-355.
5. Matching and Propensity Score
- Caliendo, M. and Kopeinig, S. (2008). Some Practical Guidance for the Implementation of Propensity Score Matching. Journal of Economic Surveys 22:1, 31-72.
- Huber, M., Lechner, M. and Wunsch, C. (2013). The Performance of Estimators Based on the Propensity Score. Journal of Econometrics 175:1, 1-22.
- *Heckman, J. J., Ichimura, H. and Todd, P. E. (1997). Matching as an Econometric Evalua-tion Estimator: Evidence from Evaluating a Job Training Programme. The Review of Economic Studies 64:4, 605-654.
- *Imbens, G. W. and Wooldridge, J. M. (2009). Recent Developments in the Economet-rics of Program Evaluation. Journal of Economic Literature 47:1, 5-86.
6. Difference-in-Differences
- Lechner, M. (2010).The Estimation of Causal Effects by Difference-in-Difference Meth-ods. Foundations and Trends in Econometrics 43:165-224.
- *Imbens, G. W. and Wooldridge, J. M. (2009). Recent Developments in the Economet-rics of Program Evaluation. Journal of Economic Literature 47:1, 5-86.
General literature:
- Angrist, J.D. and J.-S. Pischke (2009). Mostly Harmless Econometrics, Princeton Univer-sity Press.
- Imbens, G. W. and Donald Rubin (2014). Causal Inference in Statistics, Social, and Bio-medical Sciences: An Introduction, Cambridge University Press.
- Imbens, G. W. and Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature 47:1, 5-86.
Location: tba
Teaching language: English
Student evaluation: paper presentation
Application: by email to spindler@mea.mpisoc.mpg.de until July 31st