Automatic Debiased Machine Learning of Causal and Structural Effects

Automatic Debiased Machine Learning of Causal and Structural Effects

14 September 2018

Victor Chernozhukov

Papers citing "Automatic Debiased Machine Learning of Causal and Structural Effects"

18 / 18 papers shown

Title
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference Lars van der Laan David Hubbard Allen Tran Nathan Kallus Aurélien F. Bibaut OffRL 39 0 0 12 Jan 2025
Double Machine Learning for Static Panel Models with Fixed Effects Paul Clarke Annalivia Polselli 52 2 0 03 Jan 2025
Stabilized Inverse Probability Weighting via Isotonic Calibration L. Laan Ziming Lin M. Carone Alex Luedtke 33 2 0 10 Nov 2024
Debiasing Synthetic Data Generated by Deep Generative Models A. Decruyenaere Heidelinde Dehaene Paloma Rabaey Christiaan Polet Johan Decruyenaere Thomas Demeester S. Vansteelandt AI4CE 31 0 0 06 Nov 2024
Orthogonal Causal Calibration Justin Whitehouse Christopher Jung Vasilis Syrgkanis Bryan Wilder Zhiwei Steven Wu CML 114 1 0 04 Jun 2024
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation Jikai Jin Vasilis Syrgkanis CML 67 1 0 22 Feb 2024
Doubly Robust Proximal Causal Learning for Continuous Treatments Yong Wu Yanwei Fu Shouyan Wang Xinwei Sun 26 1 0 22 Sep 2023
Multiply Robust Estimator Circumvents Hyperparameter Tuning of Neural Network Models in Causal Inference Mehdi Rostami O. Saarela CML 21 0 0 20 Jul 2023
Choice Models and Permutation Invariance: Demand Estimation in Differentiated Products Markets Amandeep Singh Ye Liu Hema Yoganarasimhan 30 2 0 13 Jul 2023
The Fundamental Limits of Structure-Agnostic Functional Estimation Sivaraman Balakrishnan Edward H. Kennedy Larry A. Wasserman 20 11 0 06 May 2023
Inference on Optimal Dynamic Policies via Softmax Approximation Qizhao Chen Morgane Austern Vasilis Syrgkanis OffRL 29 1 0 08 Mar 2023
Inference on Strongly Identified Functionals of Weakly Identified Functions Andrew Bennett Nathan Kallus Xiaojie Mao Whitney Newey Vasilis Syrgkanis Masatoshi Uehara 30 15 0 17 Aug 2022
Average Adjusted Association: Efficient Estimation with High Dimensional Confounders S. Jun S. Lee 27 1 0 27 May 2022
Long Story Short: Omitted Variable Bias in Causal Machine Learning Victor Chernozhukov Carlos Cinelli Whitney Newey Amit Sharma Vasilis Syrgkanis CML 16 35 0 26 Dec 2021
Minimax Semiparametric Learning With Approximate Sparsity Jelena Bradic Victor Chernozhukov Whitney Newey Yinchu Zhu 44 21 0 27 Dec 2019
Characterization of parameters with a mixed bias property A. Rotnitzky Ezequiel Smucler J. M. Robins 14 66 0 07 Apr 2019
Debiased Inference of Average Partial Effects in Single-Index Models David A. Hirshberg Stefan Wager CML 16 13 0 06 Nov 2018
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory Adel Javanmard Andrea Montanari 107 160 0 17 Jan 2013