References
Athey, Susan, Julie Tibshirani, and Stefan Wager. 2019.
“Generalized Random Forests.” The Annals of
Statistics 47 (2): 1148–78.
Chernozhukov, Victor, 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 21 (1): C1–68. https://doi.org/10.1111/ectj.12097.
Chernozhukov, Victor, Mert Demirer, Esther Duflo, and Ivan
Fernandez-Val. 2018. “Generic Machine Learning Inference on
Heterogeneous Treatment Effects in Randomized Experiments, with an
Application to Immunization in India.” National Bureau of
Economic Research.
Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth A. Stuart. 2007.
“Matching as Nonparametric Preprocessing for Reducing Model
Dependence in Parametric Causal Inference.” Political
Analysis 15 (3): 199–236. https://doi.org/10.1093/pan/mpl013.
Iacus, Stefano M., Gary King, and Giuseppe Porro. 2012. “Causal
Inference Without Balance Checking: Coarsened Exact Matching.”
Political Analysis 20 (1): 1–24. https://doi.org/10.1093/pan/mpr013.
Imbens, Guido W. 2015. “Matching Methods in Practice: Three
Examples.” The Journal of Human Resources 50 (2):
373–419. https://www.jstor.org/stable/24735990.
Kallus, Nathan. 2022. “Treatment Effect Risk: Bounds and
Inference.” arXiv Preprint arXiv:2201.05893.
Lin, Winston. 2013. “Agnostic Notes on Regression Adjustments to
Experimental Data: Reexamining Freedman’s Critique.”
The Annals of Applied Statistics 7 (1): 295–318. https://doi.org/10.1214/12-AOAS583.
Robins, James M., and Andrea Rotnitzky. 1995. “Semiparametric
Efficiency in Multivariate Regression Models with Missing Data.”
Journal of the American Statistical Association 90 (429):
122129. https://doi.org/10.1080/01621459.1995.10476494.
Robinson, P. M. 1988. “Root-n-Consistent Semiparametric
Regression.” Econometrica 56 (4): 931–54. https://doi.org/10.2307/1912705.
Wager, Stefan, and Susan Athey. 2018. “Estimation and Inference of
Heterogeneous Treatment Effects Using Random Forests.”
Journal of the American Statistical Association 113 (523):
1228–42.