Double machine learning literature#

Main Reference

  • Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, James Robins
    Double/debiased machine learning for treatment and structural parameters
    The Econometrics Journal, 21(1), C1-C68, 2018
    URL arXiv

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-forge

  • Philipp 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 CRAN

  • Keith 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
    GitHub

  • Hugo 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 CRAN

  • Michael C. Knaus
    Double Machine Learning based Program Evaluation under Unconfoundedness
    arXiv preprint arXiv:2003.03191 [econ.EM], 2020
    R Package causalDML
    arXiv GitHub

  • Michael 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 GitHub

  • Malte 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 GitHub

  • Juraj 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
    URL

  • Harold D. Chiang, Kengo Kato, Yukun Ma, Yuya Sasaki
    Multiway Cluster Robust Double/Debiased Machine Learning
    Journal of Business & Economic Statistics, forthcoming, 2021
    URL arXiv

  • Nathan 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
    arXiv

  • Nathan Kallus, Masatoshi Uehara
    Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes
    Journal of Machine Learning Research 21, 1-63, 2020
    URL

  • Yusuke Narita, Shota Yasui, Kohei Yata
    Debiased Off-Policy Evaluation for Recommendation Systems
    RecSys ‘21: Fifteenth ACM Conference on Recommender Systems, 372–379, 2021
    URL arXiv

  • Lester Mackey, Vasilis Syrgkanis, Ilias Zadik
    Orthogonal Machine Learning: Power and Limitations
    Proceedings of the 35th International Conference on Machine Learning, 2018
    URL arXiv

  • Pedro HC Sant’Anna, Jun Zhao
    Doubly robust difference-in-differences estimators
    Journal of Econometrics, 219(1), Pages 101-122, 2020
    URL

  • Vira Semenova, Victor Chernozhukov
    Debiased machine learning of conditional average treatment effects and other causal functions
    The Econometrics Journal, 24(2), Pages 264-289, 2021
    URL

  • Vira 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
    arXiv

  • Michael 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 arXiv

  • Alexandre Belloni, Victor Chernozhukov, Lie Wang
    Pivotal estimation via square-root Lasso in nonparametric regression
    The Annals of Statistics, 42(2), 757-788, 2014
    URL

  • Victor 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
    URL

  • Adel 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 arXiv

  • Jerzy 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
    URL

  • C.-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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