Resources & References

Textbooks

Causal Inference

  • Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. Free online
  • Huntington-Klein, N. (2022). The Effect: An Introduction to Research Design and Causality. Chapman & Hall. Free online
  • Angrist, J. & Pischke, J. (2009). Mostly Harmless Econometrics. Princeton University Press.
  • Angrist, J. & Pischke, J. (2015). Mastering 'Metrics: The Path from Cause to Effect. Princeton University Press.

Programming & Data Science

  • McKinney, W. (2022). Python for Data Analysis (3rd ed.). O'Reilly. Free online
  • Wickham, H. & Grolemund, G. (2023). R for Data Science (2nd ed.). O'Reilly. Free online

Machine Learning

  • James, G., et al. (2021). An Introduction to Statistical Learning (2nd ed.). Springer. Free online
  • Hastie, T., et al. (2009). The Elements of Statistical Learning. Springer. Free online

Data Sources

SourceDescriptionLink
IPUMSHarmonized census and survey dataipums.org
World Bank WDIDevelopment indicators for 200+ countriesdata.worldbank.org
FREDEconomic time series from the Federal Reservefred.stlouisfed.org
GapminderHealth and wealth datagapminder.org
Harvard DataverseResearch data repositorydataverse.harvard.edu
AEA Data EditorReplication files for economics papersopenicpsr.org

Key Packages

Python

pandas, numpy, matplotlib, seaborn, statsmodels, linearmodels, rdrobust, scikit-learn, pytorch, transformers

Stata

estout, rdrobust, csdid, did_imputation, psmatch2, reghdfe, ftools, gtools

R

tidyverse, fixest, modelsummary, rdrobust, did, MatchIt, ggdag, renv

Online Courses & Tutorials

Acknowledgments

Special thanks to Scott Cunningham, Nick Huntington-Klein, Grant McDermott, and all contributors to open-source data science tools.