Books and compendiums can be bought in Akademika bookstore at Blindern campus. You will need a valid semester card to buy compendiums.
Books
Angrist, J. D. and Pischke, J.-S. (2015). Mastering Metrics: The Path from Cause to Effect. Princeton University Press
Online articles
Acharya, A., Blackwell, M., and Sen, M. (2016). The political legacy of american slavery. Journal of Politics, 78 (3): 621-641
Altonji, J. G., Elder, T. E., and Taber, C. R. (2005). Selection on observed and unobserved variables: Assessing the effectiveness of catholic schools. Journal of Political Economy, 113(1):151–183
Aronow, Peter M., and Cyrus Samii. (2016). "Does Regression Produce Representative Estimates of Causal Effects?." American Journal of Political Science 60.1 (2016): 250-267.
Bechtel, M. M., Hangartner, D., and Schmid, L. (2015). Does compulsory voting increase support for leftist policy? American Journal of Political Science, 60(3):752-767
Blackwell, M. (2013). A selection bias approach to sensitity analysis for causal effects. Political Analysis, 22(1):169 – 182
Cant´ u, F. and Saiegh, S. M. (2011). Fraudulent democracy? an analysis of argentina’s infamous decade using supervised machine learning. Political Analysis, 19(4):409–433
Caughey, D. and Sekhon, J. S. (2011). Elections and the regression discontinuity design: Lessons from close u.s. house races, 1942 - 2008. Political Analysis, 19:385
Chadefaux, T. (2014). Early warning signals for war in the news. Journal of Peace Research, 51(1):5–18
Diermeier, D., Godbout, J.-F., Yu, B., and Kaufmann, S. (2012). Language and ideology in congress. British Journal of Political Science, 42(01):31–55
Finseraas, H. (2015). The effect of a booming local economy in early childhood on the propensity to vote: Evidence from a natural experiment. British Journal of Political Science, FirstView:1–21
Hill, D.W. and Jones, Z. M. (2014). An empirical evaluation of explanations for state repression. American Political Science Review, 108(03):661–687
Iacus, S. M., King, G., and Porro, G. (2012). Causal inference without balance checking: Coarsed exact matching. Political Analysis, 20(1):1 – 24
Imai, K., Keele, L., Tingley, D., and Yamamoto, T. (2011). Unpacking the black box of causality: Learning about causal mechanisms from experimental and observational studies. American Political Science Review, 105(4):765 – 789
Keele, L. and Minozzi, W. (2013). How much is minnesota like wisconsin? assumptions and counterfactuals in causal inference with observational data. Political Analysis, 21(1):193 – 216
Keele, L. (2015). The statistics of causal inference: A view from political methodology. Political Analysis, 23(3):313 – 335
King, G. and Zeng, L. (2007). When can history be our guide? the pitfalls of counterfactual inference. International Studies Quarterly, 51(1):183 – 210
King, G., Lucas, C. and A. Nielsen, R. (2016), The Balance-Sample Size Frontier in Matching Methods for Causal Inference. American Journal of Political Science. doi:10.1111/ajps.12272
Longo, M., Canetti, D., and Hite-Rubin, N. (2014). A checkpoint effect? evidence from a natural experiment on travel restrictions in the west bank. American Journal of Political Science, 58(4):1006–1023
Muchlinski, D., Siroky, D., He, J., and Kocher, M. (2016). Comparing random forest with logistic regression for predicting class-imbalanced civil war onset data. Political Analysis, 24(1):87–103
Murray, M. P. (2006). Avoiding invalid instruments and coping with weak instruments. The journal of economic perspectives, 20(4):111–132
Samii, C. (2016). Causal empiricism in quantitative research. The Journal of Politics, 78(3):000–000
Sekhol, J. S. (2009). Opiates for the matches: Matching methods for causal inference. Annual Review of Political Science, 12:487 – 508
Shmueli, G. (2010). To explain or to predict? Statistical science, August 2010, Vol.25(3), pp.289-310
Skovron, Christopher, and Rocío Titiunik. (2016). "A practical guide to regression discontinuity designs in political science". Working paper. Link: http://www-personal.umich.edu/~titiunik/papers/SkovronTitiunik2015.pdf
Sovey, A. J. and Green, D. P. (2011). Instrumental variables estimation in political science: A readers’ guide. American Journal of Political Science, 55(1):188 – 200
Ward, M. D., Greenhill, B. D., and Bakke, K. M. (2010). The perils of policy by p-value: Predicting civil conflicts. Journal of Peace Research, 47(4):363 – 375
Extra non-compulsory Reading
This literature is not part of the required reading. The purpose of the recommended reading is to broaden and deepen the understanding of the subjects addressed in the course.
Angrist, J. and Pischke, J. S. (2010). The credibility revolution in empirical economics: ow better research design is taking the con out of econometrics. Journal of Economic Perspectives, 24(2):3
Clarke, K. A. (2009). Return of the phantom menace: Omitted variable bias in political research. Conflict Management and Peace Science, 26(1):46–66
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An introduction to statistical learning, volume 6. Springer. Link to free online version here.
Keele, L. and Stevenson, R. T. (2014). The perils of the all cause model. Working Paper
Keele, L. and Titiunik, R. (2015). Geographic boundaries as regression discontinuities. Political Analysis, 23(1):127 – 155
Miguel, E., Satyanath, S., and Sergenti, E. (2004). Economic shocks and civil conflict: An instrumental variables approach. Journal of political Economy, 112(4):725–753
Morgan, S. L. and Winship, C. (2015). Counterfactuals and Causal Inferences: Methods and Principles for Social Research. Analytical Methods for Social Research. Cambridge University Press, second edition (Chapter 8, pages 267 - 290)
Online articles:
- are available through the University Library databases.
- are limited to computers that are connected UiO's network. You can get access from home and when travelling.
- are easy to find. Tutorials and help to access journals and other restricted library Resources.