References

Every paper, book, and software citation in this guide, sorted alphabetically by first-author surname. Click an entry's anchor to share a deep link, or follow the arXiv / DOI / URL for the source.

34 entries. Generated from guides/shared/references.bib at build time via scripts/build-bib.mjs.

  1. [angrist2009mostly] Joshua D Angrist and Jörn-Steffen Pischke (2009). Mostly harmless econometrics: An empiricist's companion. Princeton University Press.
  2. [athey2016recursive] Susan Athey and Guido Imbens (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences. Vol. 113, no. 27. pp. 7353-7360.
  3. [athey2019machine] Susan Athey and Guido W Imbens (2019). Machine learning methods that economists should know about. Annual Review of Economics. Vol. 11. pp. 685-725.
  4. [bach2022doubleml] Philipp Bach, Victor Chernozhukov, Malte S Kurz, and Martin Spindler (2022). DoubleML–An object-oriented implementation of double machine learning in R. Journal of Statistical Software. Vol. 108. pp. 1-56.
  5. [bojinov2019time] Iavor Bojinov and Neil Shephard (2019). Time series experiments and causal estimands: Exact randomization tests and trading. Journal of the American Statistical Association. Vol. 114, no. 528. pp. 1665-1682.
  6. [chernozhukov2017double] Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, and Whitney Newey (2017). Double machine learning for treatment and causal parameters. arXiv preprint arXiv:1608.00060.
  7. [chernozhukov2018double] Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal. Vol. 21, no. 1. pp. C1-C68.
  8. [chernozhukov2022locally] Victor Chernozhukov, Juan Carlos Escanciano, Hidehiko Ichimura, Whitney K Newey, and James M Robins (2022). Locally robust semiparametric estimation. Econometrica. Vol. 90, no. 4. pp. 1501-1535.
  9. [cinelli2020omitted] Carlos Cinelli and Chad Hazlett (2020). Making Sense of Sensitivity: Extending Omitted Variable Bias. Journal of the Royal Statistical Society: Series B. Vol. 82, no. 1. pp. 39-67.
  10. [dehejia1999causal] Rajeev H Dehejia and Sadek Wahba (1999). Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs. Journal of the American Statistical Association. Vol. 94, no. 448. pp. 1053-1062.
  11. [facure2022causal] Matheus Facure (2022). Causal inference for the brave and true. link.
  12. [fred] Federal Reserve Bank of St. Louis (2023). Federal Reserve Economic Data (FRED). link.
  13. [frisch1933partial] Ragnar Frisch and Frederick V Waugh (1933). Partial time regressions as compared with individual trends. Econometrica. Vol. 1, no. 4. pp. 387-401.
  14. [hernan2020causal] Miguel A Hernán and James M Robins (2020). Causal inference: What if. Chapman & Hall/CRC.
  15. [holland1986statistics] Paul W Holland (1986). Statistics and causal inference. Journal of the American Statistical Association. Vol. 81, no. 396. pp. 945-960.
  16. [huntington2021effect] Nick Huntington-Klein (2021). The effect: An introduction to research design and causality. CRC Press.
  17. [imai2023should] Kosuke Imai and In Song Kim (2021). When should we use unit fixed effects regression models for causal inference with longitudinal data?. American Journal of Political Science. Vol. 65, no. 2. pp. 467-490.
  18. [imbens2003sensitivity] Guido W Imbens (2003). Sensitivity to Exogeneity Assumptions in Program Evaluation. American Economic Review. Vol. 93, no. 2. pp. 126-132.
  19. [imbens2015causal] Guido W Imbens and Donald B Rubin (2015). Causal inference for statistics, social, and biomedical sciences: An introduction. Cambridge University Press.
  20. [lalonde1986evaluating] Robert J LaLonde (1986). Evaluating the econometric evaluations of training programs with experimental data. The American Economic Review. Vol. 76, no. 4. pp. 604-620.
  21. [lovell1963seasonal] Michael C Lovell (1963). Seasonal adjustment of economic time series and multiple regression analysis. Journal of the American Statistical Association. Vol. 58, no. 304. pp. 993-1010.
  22. [econml] Microsoft Research (2019). EconML: A Python package for ML-based heterogeneous treatment effects estimation. link.
  23. [newey1987] Whitney K Newey and Kenneth D West (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica. Vol. 55, no. 3. pp. 703-708.
  24. [neyman1959optimal] Jerzy Neyman (1959). Optimal asymptotic tests of composite statistical hypotheses. Probability and statistics. pp. 213-234.
  25. [nie2021quasi] Xinkun Nie and Stefan Wager (2021). Quasi-oracle estimation of heterogeneous treatment effects. Biometrika. Vol. 108, no. 2. pp. 299-319.
  26. [pearl2009causality] Judea Pearl (2009). Causality: Models, reasoning, and inference. Cambridge University Press.
  27. [dePrado2018] Marcos Lopez Prado (2018). Advances in Financial Machine Learning. John Wiley & Sons.
  28. [rosenbaum2002observational] Paul R Rosenbaum (2002). Observational Studies. Springer.
  29. [rosenbaum2010design] Paul R Rosenbaum (2010). Design of Observational Studies. Springer.
  30. [rubin1974estimating] Donald B Rubin (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology. Vol. 66, no. 5. pp. 688-701.
  31. [dowhy2022] Amit Sharma and Emre Kiciman (2020). DoWhy: An end-to-end library for causal inference. arXiv preprint arXiv:2011.04216.
  32. [van2000asymptotic] Aad W Vaart (2000). Asymptotic statistics. Cambridge University Press.
  33. [vanderweele2017evalue] Tyler J VanderWeele and Peng Ding (2017). Sensitivity Analysis in Observational Research: Introducing the E-Value. Annals of Internal Medicine. Vol. 167, no. 4. pp. 268-274.
  34. [wager2018estimation] Stefan Wager and Susan Athey (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association. Vol. 113, no. 523. pp. 1228-1242.