Referências

Todas as obras citadas nas anotações.

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Angrist, Joshua D., e Jörn-Steffen Pischke. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.

Angrist, Joshua D., e Jörn-Steffen Pischke. 2015. Mastering ‘Metrics: The Path from Cause to Effect. Princeton University Press.

Bruce, Raphael, Braz Carrillo, Jesse M. Cunha, e Carolina González-Velosa. 2022. “Under Pressure: Women’s Leadership during the COVID-19 Crisis”. Journal of Development Economics 154. https://doi.org/10.1016/j.jdeveco.2021.102761.

Callaway, Brantly, e Pedro H. C. Sant’Anna. 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.

Cinelli, Carlos, Andrew Forney, e Judea Pearl. 2024. “A Crash Course in Good and Bad Controls”. Sociological Methods & Research 53 (3): 1071–104.

Cunningham, Scott. 2021. Causal Inference: The Mixtape. Yale University Press.

Elwert, Felix. 2013. “Graphical Causal Models”. Em Handbook of Causal Analysis for Social Research, organizado por Stephen L. Morgan. Springer. https://doi.org/10.1007/978-94-007-6094-3_13.

Gelman, Andrew, e Guido Imbens. 2019. “Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs”. Journal of Business & Economic Statistics 37 (3): 447–56. https://doi.org/10.1080/07350015.2017.1366909.

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Ho, Daniel E., Kosuke Imai, Gary King, e Elizabeth A. Stuart. 2007. “Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference”. Political Analysis 15: 199–236. https://doi.org/10.1093/pan/mpl013.

Huntington-Klein, Nick. 2022. The Effect: An Introduction to Research Design and Causality. Chapman & Hall.

Iacus, Stefano M., Gary King, e Giuseppe Porro. 2012. “Causal Inference Without Balance Checking: Coarsened Exact Matching”. Political Analysis 20 (1): 1–24. https://doi.org/10.1093/pan/mpr013.

Imai, Kosuke, Gary King, e Elizabeth A. Stuart. 2008. “Misunderstanding Between Experimentalists and Observationalists About Causal Inference”. Journal of the Royal Statistical Society: Series A 171 (2): 481–502. https://doi.org/10.1111/j.1467-985x.2007.00527.x.

King, Gary, e Richard Nielsen. 2019. “Why Propensity Scores Should Not Be Used for Matching”. Political Analysis 27 (4): 1–20. https://doi.org/10.1017/pan.2019.11.

King, Gary, e Langche Zeng. 2006. “The Dangers of Extreme Counterfactuals”. Political Analysis 14: 131–59. https://doi.org/10.1093/pan/mpj004.

Morgan, Stephen L., e Christopher Winship. 2015. Counterfactuals and Causal Inference: Methods and Principles for Social Research. 2º ed. Cambridge University Press.

Pearl, Judea, Madelyn Glymour, e Nicholas P. Jewell. 2016. Causal Inference in Statistics: A Primer. Wiley.

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Wooldridge, Jeffrey M. 2013. Introductory Econometrics: A Modern Approach. 5º ed. South-Western Cengage Learning.