Further reading

Key resources for “Statistical Methods in Public Policy Research”

1 Brief History of Statistics in Public Policy

2 Core Methodological Approaches

2.1 Description

  • Healy, K. (2018). Data visualization: A practical introduction. Princeton University Press. http://socviz.co/
  • Healy, K., & Moody, J. (2014). Data visualization in sociology. Annual Review of Sociology, 40, 105–128. https://doi.org/10.1146/annurev-soc-071312-145551
  • Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science: Import, tidy, transform, visualize, and model data (2nd ed.). O’Reilly.

2.2 Explanation

2.2.1 Estimation, Inference, and Hypothesis Testing

  • Imbens, G. W. (2021). Statistical significance, p-values, and the reporting of uncertainty. Journal of Economic Perspectives, 35(3), 157–174. https://doi.org/10.1257/jep.35.3.157
  • Lundberg, I., Johnson, R., & Stewart, B. M. (2021). What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory. American Sociological Review, 86(3), 532–565. https://doi.org/10.1177/00031224211004187
  • Little, R. J., & Lewis, R. J. (2021). Estimands, Estimators, and Estimates. JAMA : The Journal of the American Medical Association, 326(10), 967–968. https://doi.org/10.1001/jama.2021.2886

2.2.2 Causal Attribution and Causal Inference

  • Pearl, J., & Mackenzie, D. (2020). The book of why: The new science of cause and effect. Basic Books.
  • Greifer, N., & Stuart, E. A. (2023). Choosing the causal estimand for propensity score analysis of observational studies (arXiv:2106.10577). arXiv. https://doi.org/10.48550/arXiv.2106.10577
  • Huntington-Klein, N. (2021). The effect: An introduction to research design and causality. Chapman and Hall / CRC. https://doi.org/10.1201/9781003226055
  • Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42. https://doi.org/10.1177/2515245917745629
  • Cunningham, S. (2021). Causal inference: The mixtape. Yale University Press. https://mixtape.scunning.com/
  • Hernán, M. A., & Robins, J. M. (2024). Causal inference: What if. Chapman and Hall / CRC. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
  • Angrist, J. D., & Pischke, J.-S. (2008). Mostly harmless econometrics: An empiricist’s companion. Princeton University Press.
  • Angrist, J. D., & Pischke, J.-S. (2015). Mastering ’metrics: The path from cause to effect. Princeton University Press.

2.3 Prediction

3 The Pitfalls of Counting, Gathering, and Learning from Public Data

  • Fourcade, M., & Healy, K. (2024). The ordinal society. Harvard University Press. https://doi.org/10.4159/9780674296688
  • Thoma, J. (2024). Social science, policy and democracy. Philosophy & Public Affairs, 52(1), 5–41. https://doi.org/10.1111/papa.12250
  • Broussard, M. (2023). More than a glitch: Confronting race, gender, and ability bias in tech. The MIT Press.
  • Criado-Perez, C. (2020). Invisible women: Exposing data bias in a world designed for men. Vintage.
  • D’Ignazio, C., & Klein, L. F. (2020). Data feminism. The MIT Press.
  • Pahlka, J. (2023). Recoding America: Why government is failing in the digital age and how we can do better. Metropolitan Books.
  • Alkhatib, A., & Bernstein, M. (2019). Street-level algorithms: A theory at the gaps between policy and decisions. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 530:1–530:13. https://doi.org/10.1145/3290605.3300760

References

Alkhatib, A., & Bernstein, M. (2019). Street-level algorithms: A theory at the gaps between policy and decisions. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 530:1–530:13. https://doi.org/10.1145/3290605.3300760
Angrist, J. D., & Pischke, J.-S. (2008). Mostly harmless econometrics: An empiricist’s companion. Princeton University Press.
Angrist, J. D., & Pischke, J.-S. (2015). Mastering ’metrics: The path from cause to effect. Princeton University Press.
Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11(1), 685–725. https://doi.org/10.1146/annurev-economics-080217-053433
Breunig, C., & Ahlquist, J. S. (2014). Quantitative methodologies in public policy. In I. Engeli & C. R. Allison (Eds.), Comparative Policy Studies (pp. 109–129). Palgrave Macmillan UK. https://doi.org/10.1057/9781137314154_6
Broussard, M. (2023). More than a glitch: Confronting race, gender, and ability bias in tech. The MIT Press.
Criado-Perez, C. (2020). Invisible women: Exposing data bias in a world designed for men. Vintage.
Cunningham, S. (2021). Causal inference: The mixtape. Yale University Press. https://mixtape.scunning.com/
D’Ignazio, C., & Klein, L. F. (2020). Data feminism. The MIT Press.
Fourcade, M., & Healy, K. (2024). The ordinal society. Harvard University Press. https://doi.org/10.4159/9780674296688
Greifer, N., & Stuart, E. A. (2023). Choosing the causal estimand for propensity score analysis of observational studies (arXiv:2106.10577). arXiv. https://doi.org/10.48550/arXiv.2106.10577
Healy, K. (2018). Data visualization: A practical introduction. Princeton University Press. http://socviz.co/
Healy, K., & Moody, J. (2014). Data visualization in sociology. Annual Review of Sociology, 40, 105–128. https://doi.org/10.1146/annurev-soc-071312-145551
Hernán, M. A., & Robins, J. M. (2024). Causal inference: What if. Chapman and Hall / CRC. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
Huntington-Klein, N. (2021). The effect: An introduction to research design and causality. Chapman and Hall / CRC. https://doi.org/10.1201/9781003226055
Imbens, G. W. (2021). Statistical significance, p-values, and the reporting of uncertainty. Journal of Economic Perspectives, 35(3), 157–174. https://doi.org/10.1257/jep.35.3.157
Little, R. J., & Lewis, R. J. (2021). Estimands, Estimators, and Estimates. JAMA : The Journal of the American Medical Association, 326(10), 967–968. https://doi.org/10.1001/jama.2021.2886
Lundberg, I., Johnson, R., & Stewart, B. M. (2021). What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory. American Sociological Review, 86(3), 532–565. https://doi.org/10.1177/00031224211004187
Nowlin, M. C., & Wehde, W. (2024). Teaching quantitative methods to students of public policy. In Handbook of Teaching Public Policy (pp. 168–180). Edward Elgar Publishing. https://www.elgaronline.com/edcollchap/book/9781800378117/book-part-9781800378117-22.xml
Pahlka, J. (2023). Recoding America: Why government is failing in the digital age and how we can do better. Metropolitan Books.
Pearl, J., & Mackenzie, D. (2020). The book of why: The new science of cause and effect. Basic Books.
Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42. https://doi.org/10.1177/2515245917745629
Scott, J. C. (1998). Seeing like a state: How certain schemes to improve the human condition have failed. Yale University Press.
Thoma, J. (2024). Social science, policy and democracy. Philosophy & Public Affairs, 52(1), 5–41. https://doi.org/10.1111/papa.12250
Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science: Import, tidy, transform, visualize, and model data (2nd ed.). O’Reilly.