Double machine learning literature#
Main Reference
Software for double machine learning
Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler
DoubleML – An Object-Oriented Implementation of Double Machine Learning in Python
Journal of Machine Learning Research, 23(53): 1-6, 2022
Python Package DoubleML
URL arXiv GitHub PyPI conda-forgePhilipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler
DoubleML – An Object-Oriented Implementation of Double Machine Learning in R
arXiv preprint arXiv:2103.09603 [stat.ML], 2021
R Package DoubleML
arXiv GitHub CRANKeith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Paul Oka, Miruna Oprescu, Vasilis Syrgkanis
EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation
2019
Python Package EconML
GitHubHugo Bodory, Martin Huber
The causalweight package for causal inference in R
Working Papers SES 493, Faculty of Economics and Social Science, University of Fribourg, 2018
R Package causalweight
URL CRANMichael C. Knaus
Double Machine Learning based Program Evaluation under Unconfoundedness
arXiv preprint arXiv:2003.03191 [econ.EM], 2020
R Package causalDML
arXiv GitHubMichael C. Knaus
A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student’s Skills
Journal of the Royal Statistical Society A, 184(1), 282-300, 2021
R Package dmlmt
URL arXiv GitHubMalte S. Kurz
Distributed Double Machine Learning with a Serverless Architecture
In Companion of the ACM/SPEC International Conference on Performance Engineering (ICPE ‘21). Association for Computing Machinery, New York, NY, USA, 27-33, 2021
Python Package DoubleML-Serverless
URL arXiv GitHubJuraj Szitas
postDoubleR: Post Double Selection with Double Machine Learning
2019
R Package postDoubleR
GitHub
Double machine learning models and methodological extensions
Neng-Chieh Chang
Double/debiased machine learning for difference-in-differences models
The Econometrics Journal, 23(2), Pages 177–191, 2020
URLHarold D. Chiang, Kengo Kato, Yukun Ma, Yuya Sasaki
Multiway Cluster Robust Double/Debiased Machine Learning
Journal of Business & Economic Statistics, forthcoming, 2021
URL arXivNathan Kallus, Xiaojie Mao, Masatoshi Uehara
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond
arXiv preprint arXiv:1912.12945 [stat.ML], 2019
arXivNathan Kallus, Masatoshi Uehara
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes
Journal of Machine Learning Research 21, 1-63, 2020
URLYusuke Narita, Shota Yasui, Kohei Yata
Debiased Off-Policy Evaluation for Recommendation Systems
RecSys ‘21: Fifteenth ACM Conference on Recommender Systems, 372–379, 2021
URL arXivLester Mackey, Vasilis Syrgkanis, Ilias Zadik
Orthogonal Machine Learning: Power and Limitations
Proceedings of the 35th International Conference on Machine Learning, 2018
URL arXivPedro HC Sant’Anna, Jun Zhao
Doubly robust difference-in-differences estimators
Journal of Econometrics, 219(1), Pages 101-122, 2020
URLVira Semenova, Victor Chernozhukov
Debiased machine learning of conditional average treatment effects and other causal functions
The Econometrics Journal, 24(2), Pages 264-289, 2021
URLVira Semenova, Matt Goldman, Victor Chernozhukov, Matt Taddy
Estimation and Inference on Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels
arXiv preprint arXiv:1712.09988 [stat.ML], 2017
arXivMichael Zimmert
Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding
arXiv preprint arXiv:1809.01643 [econ.EM], 2018
arXiv
Debiased sparsity-based inference / theoretical foundations
A. Belloni, V. Chernozhukov, C. Hansen
Inference for High-Dimensional Sparse Econometric Models
In D. Acemoglu, M. Arellano, & E. Dekel (Eds.), Advances in Economics and Econometrics: Tenth World Congress, 245-295, 2013
URL arXivAlexandre Belloni, Victor Chernozhukov, Lie Wang
Pivotal estimation via square-root Lasso in nonparametric regression
The Annals of Statistics, 42(2), 757-788, 2014
URLVictor Chernozhukov, Christian Hansen, Martin Spindler
Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach Annual Review of Economics 7(1), 649-688, 2015
URLAdel Javanmard, Andrea Montanari
Hypothesis Testing in High-Dimensional Regression Under the Gaussian Random Design Model: Asymptotic Theory
IEEE Transactions on Information Theory, 60(10):6522–6554, 2014
URL arXivJerzy Neyman
Optimal asymptotic tests of composite hypotheses
In Ulf Grenander (Eds.), Probability and Statistics, Almqvist & Wiksell, 213–234, 1959
Sara van de Geer, Peter Bühlmann, Ya’acov Ritov, Ruben Dezeure
On asymptotically optimal confidence regions and tests for high-dimensional models
The Annals of Statistics, 42(3), 1166-1202, 2014
URLC.-H. Zhang, S.S. Zhang
Confidence intervals for low dimensional parameters in high dimensional linear models
Journal of the Royal Statistical Society: Series B, 76, 217-242, 2014
URL
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