参考文献

Chernozhukov2016

V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, and a. W. Newey. Double Machine Learning for Treatment and Causal Parameters. ArXiv e-prints, 2016 年 7 月.

Chernozhukov2017

V. Chernozhukov, M. Goldman, V. Semenova, and M. Taddy. Orthogonal Machine Learning for Demand Estimation: High Dimensional Causal Inference in Dynamic Panels. ArXiv e-prints, 2017 年 12 月.

Chernozhukov2018

V. Chernozhukov, D. Nekipelov, V. Semenova, and V. Syrgkanis. Two-Stage Estimation with a High-Dimensional Second Stage. 2018.

Hartford2017

Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. Deep IV: A flexible approach for counterfactual prediction. 第 34 届国际机器学习大会论文集, 2017.

Jaggi2010

Martin Jaggi and Marek Sulovský. A simple algorithm for nuclear norm regularized problems. 第 27 届国际机器学习大会 (ICML-10) 论文集，2010 年 6 月 21-24 日，以色列海法, 页面 471–478, 2010.

Kunzel2017

Sören R Künzel, Jasjeet S Sekhon, Peter J Bickel, and Bin Yu. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv 预印本 arXiv:1706.03461, 2017. URL http://arxiv.org/abs/1706.03461.

Mackey2017

Lester W. Mackey, Vasilis Syrgkanis, and Ilias Zadik. Orthogonal machine learning: Power and limitations. CoRR, abs/1711.00342, 2017. URL http://arxiv.org/abs/1711.00342.

Newey2003

W. K. Newey and J. L. Powell. Instrumental variable estimation of nonparametric models. Econometrica, 71 (5): 1565–1578, 2003.

Foster2019

D. Foster and V. Syrgkanis. Orthogonal Statistical Learning. arXiv 预印本 arXiv:1901.09036, 2019. URL http://arxiv.org/abs/1901.09036.

Wager2018

S. Wager and S. Athey. Estimation and inference of heterogeneous treatment effects using random forests. 美国统计协会期刊, 113(523), 页码 1228-1242, 2018.

Athey2019

S. Athey, J. Tibshirani and S. Wager. Generalized Random Forests. 统计学年鉴, 2019

Oprescu2019

M. Oprescu, V. Syrgkanis and Z. S. Wu. Orthogonal Random Forest for Causal Inference. 第 36 届国际机器学习大会论文集, 2019. URL http://proceedings.mlr.press/v97/oprescu19a.html.

Nie2017

X. Nie and S. Wager. Quasi-Oracle Estimation of Heterogeneous Treatment Effects. arXiv 预印本 arXiv:1712.04912, 2017. URL http://arxiv.org/abs/1712.04912.

Buhlmann2011

P. Bühlmann and S. van de Geer Statistics for High-Dimensional Data Springer Series in Statistics, 2011 URL https://www.springer.com/gp/book/9783642201912

Robins1994

Robins, J.M., Rotnitzky, A., and Zhao, L.P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association 89,846–866.

Bang

Bang, H. and Robins, J.M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics 61,962–972.

Tsiatis

Tsiatis AA (2006). Semiparametric Theory and Missing Data. New York: Springer; 2006.

Dudik2014

Dudík, M., Erhan, D., Langford, J., & Li, L. (2014). Doubly robust policy evaluation and optimization. Statistical Science, 29(4), 485-511.

Athey2017

Athey, S., & Wager, S. (2017). Efficient policy learning. arXiv 预印本 arXiv:1702.02896.

Friedberg2018

Friedberg, R., Tibshirani, J., Athey, S., & Wager, S. (2018). Local linear forests. arXiv 预印本 arXiv:1807.11408.

Lundberg2017

Lundberg, S., Lee, S. (2017). A Unified Approach to Interpreting Model Predictions. URL https://arxiv.org/abs/1705.07874

Lewis2021

Lewis, G., Syrgkanis, V. (2021). Double/Debiased Machine Learning for Dynamic Treatment Effects. URL https://arxiv.org/abs/2002.07285

Hernan2010

Hernán, Miguel A., and James M. Robins (2010). Causal inference. URL https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/

Syrgkanis2019

Syrgkanis, V., Lei, V., Oprescu, M., Hei, M., Battocchi, K., Lewis, G. (2019) Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments URL https://arxiv.org/abs/1905.10176