References

The bibliography below collects every in-text citation across the book. Each chapter’s “Further reading” section provides curated entry points; this page is the consolidated index, rendered in APA 7 style.

Abadie, A. (2021). Using synthetic controls: Feasibility, data requirements, and methodological aspects. Journal of Economic Literature, 59(2), 391–425. https://www.aeaweb.org/articles?id=10.1257/jel.20191450

Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105(490), 493–505. https://www.aeaweb.org/articles?id=10.1257/jasa.2010.ap08746

Abadie, A., & Gardeazabal, J. (2003). The economic costs of conflict: A case study of the Basque country. American Economic Review, 93(1), 113–132.

Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021). Synthetic difference-in-differences. American Economic Review, 111(12), 4088–4118. https://doi.org/10.1257/aer.20190159

Athey, S., Bayati, M., Doudchenko, N., Imbens, G., & Khosravi, K. (2021). Matrix completion methods for causal panel data models. Journal of the American Statistical Association, 116(536), 1716–1730. https://doi.org/10.1080/01621459.2021.1891924

Bai, J. (2003). Inferential theory for factor models of large dimensions. Econometrica, 71(1), 135–171. https://doi.org/10.1111/1468-0262.00392

Bai, J. (2009). Panel data models with interactive fixed effects. Econometrica, 77(4), 1229–1279. https://doi.org/10.3982/ECTA6135

Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: A tutorial. International Journal of Epidemiology, 46(1), 348–355. https://academic.oup.com/ije/article/46/1/348/2622842

Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? Quarterly Journal of Economics, 119(1), 249–275. https://doi.org/10.1162/003355304772839588

Brodersen, K. H. (2015). Inferring the effect of an event using CausalImpact. YouTube talk. https://youtu.be/GTgZfCltMm8

Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9, 247–274. https://research.google.com/pubs/pub41854.html

Brodersen, K. H., & Hauser, A. (2014). CausalImpact — an R package for causal inference using Bayesian structural time-series models. https://google.github.io/CausalImpact/.

Buuren, S. van, & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67. https://doi.org/10.18637/jss.v045.i03

Callaway, B. (2022). Difference-in-differences for policy evaluation. In K. F. Zimmermann (Ed.), Handbook of labor, human resources and population economics (pp. 1–61). Springer. https://doi.org/10.1007/978-3-319-57365-6_352-1

Callaway, B., & Sant’Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200–230. https://doi.org/10.1016/j.jeconom.2020.12.001

Card, D., & Krueger, A. B. (1994). Minimum wages and employment: A case study of the fast-food industry in New Jersey and Pennsylvania. American Economic Review, 84(4), 772–793. https://www.jstor.org/stable/2118030

Carvalho, C. M., Polson, N. G., & Scott, J. G. (2010). The horseshoe estimator for sparse signals. Biometrika, 97(2), 465–480. https://doi.org/10.1093/biomet/asq017

Cattaneo, M. D., Feng, Y., Palomba, F., & Titiunik, R. (2025). scpi: Uncertainty quantification for synthetic control methods. Journal of Statistical Software, 113(1), 1–38. https://doi.org/10.18637/jss.v113.i01

Cattaneo, M. D., Feng, Y., & Titiunik, R. (2021). Prediction intervals for synthetic control methods. Journal of the American Statistical Association, 116(536), 1865–1880. https://doi.org/10.1080/01621459.2021.1979561

Chaisemartin, C. de, & D’Haultfœuille, X. (2020). Two-way fixed effects estimators with heterogeneous treatment effects. American Economic Review, 110(9), 2964–2996. https://doi.org/10.1257/aer.20181169

Clarke, D., Pailañir, D., Athey, S., & Imbens, G. (2023). Synthetic difference-in-differences estimation (IZA Discussion Paper No. 15907). Institute of Labor Economics (IZA). https://docs.iza.org/dp15907.pdf

Dunford, E. (2024). tidysynth — a tidy implementation of the synthetic control method in R. GitHub repository. https://github.com/edunford/tidysynth

George, E. I., & McCulloch, R. E. (1997). Approaches for Bayesian variable selection. Statistica Sinica, 7(2), 339–373. https://www.jstor.org/stable/24306083

Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics, 225(2), 254–277. https://doi.org/10.1016/j.jeconom.2021.03.014

Hirshberg, D. A., Arkhangelsky, D., Athey, S., Imbens, G. W., & Wager, S. (2021). synthdid: Synthetic difference in differences estimation in R. GitHub repository. https://github.com/synth-inference/synthdid

Hyndman, R. J., & Athanasopoulos, G. (2020). fpp3 — forecasting: Principles and practice (3rd edition) data and R package. https://cran.r-project.org/package=fpp3.

Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts. https://otexts.com/fpp3/

Kranz, S. (2022a). Covariate-augmented synthetic difference-in-differences via xsynthdid. R package documentation. https://github.com/skranz/xsynthdid

Kranz, S. (2022b). xsynthdid: Covariate-augmented synthetic difference-in-differences in R. r-universe / GitHub repository. https://github.com/skranz/xsynthdid

LeSage, J. P., & Pace, R. K. (2009). Introduction to spatial econometrics. Chapman & Hall/CRC. https://www.routledge.com/Introduction-to-Spatial-Econometrics/LeSage-Pace/p/book/9781420064247

Liu, L., Wang, Y., & Xu, Y. (2024). A practical guide to counterfactual estimators for causal inference with time-series cross-sectional data. American Journal of Political Science, 68(1), 160–176. https://doi.org/10.1111/ajps.12723

ODISSEI Social Data Science team. (2024). Workshop on causal effects of policy interventions. https://causalpolicy.nl/.

Rambachan, A., & Roth, J. (2023). A more credible approach to parallel trends. Review of Economic Studies, 90(5), 2555–2591. https://doi.org/10.1093/restud/rdad018

Roth, J., Sant’Anna, P. H. C., Bilinski, A., & Poe, J. (2023). What’s trending in difference-in-differences? A synthesis of the recent econometrics literature. Journal of Econometrics, 235(2), 2218–2244. https://doi.org/10.1016/j.jeconom.2023.03.008

Sakaguchi, S., & Tagawa, H. (2026). Identification and Bayesian inference for synthetic control methods with spillover effects. The Econometrics Journal. https://doi.org/10.1093/ectj/utag006

Scott, S. L., & Varian, H. R. (2014). Predicting the present with Bayesian structural time series. International Journal of Mathematical Modelling and Numerical Optimisation, 5(1–2), 4–23. https://doi.org/10.1504/IJMMNO.2014.059942

Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175–199. https://doi.org/10.1016/j.jeconom.2020.09.006

Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27(4), 299–309. https://doi.org/10.1046/j.1365-2710.2002.00430.x

Xu, Y. (2017). Generalized synthetic control method: Causal inference with interactive fixed effects models. Political Analysis, 25(1), 57–76. https://doi.org/10.1017/pan.2016.2