This repository contains all the slides for the course. We may update these slides as we go along, so this repo will always store the latest version.
All the code/ required for the workshops will be uploaded a day in advance, and all complementary readings are listed below.
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Grimmer, J., Roberts, M. E., & Stewart, B. M. (2021). Machine learning for social science: An agnostic approach. Annual Review of Political Science, 24, 395-419.
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Knox, D., Lucas, C., & Cho, W. K. T. (2022). Testing causal theories with learned proxies. Annual Review of Political Science, 25, 419-441.
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Spirling, A. & Stewart, B. M. (2022). What good is a regression? Inference to the best explanation and the practise of political science research. Working paper.
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Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015). Prediction policy problems. American Economic Review, 105(5), 491-495.
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Kim, I. S. (2017). Political cleavages within industry: Firm-level lobbying for trade liberalization. American Political Science Review, 111(1), 1-20.
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Blackwell, M., & Olson, M. P. (2022). Reducing model misspecification and bias in the estimation of interactions. Political Analysis, 30(4), 495-514.
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Montgomery, J. M., & Olivella, S. (2018). Tree‐Based Models for Political Science Data. American Journal of Political Science, 62(3), 729-744.
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Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1), 217-240.
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Athey, S., & Wager, S. (2019). Estimating treatment effects with causal forests: An application. Observational studies, 5(2), 37-51.
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Lall, R., & Robinson, T. (2022). The MIDAS touch: accurate and scalable missing-data imputation with deep learning. Political Analysis, 30(2), 179-196.
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Bellodi, L. (2022). A dynamic measure of bureaucratic reputation: New data for new theory. American Journal of Political Science.
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Gal, Y., & Ghahramani, Z. (2016, June). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning (pp. 1050-1059). PMLR.
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Grimmer, J., Messing, S., & Westwood, S. J. (2017). Estimating heterogeneous treatment effects and the effects of heterogeneous treatments with ensemble methods. Political Analysis, 25(4), 413-434.
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Hare, C., & Kutsuris, M. (2023). Measuring swing voters with a supervised machine learning ensemble. Political Analysis, 31(4), 537-553.