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Debiased Machine Learning of Conditional Average Treatment Effects and
  Other Causal Functions
v1v2v3v4v5v6 (latest)

Debiased Machine Learning of Conditional Average Treatment Effects and Other Causal Functions

21 February 2017
Victor Chernozhukov
Vira Semenova
    CML
ArXiv (abs)PDFHTML

Papers citing "Debiased Machine Learning of Conditional Average Treatment Effects and Other Causal Functions"

6 / 6 papers shown
Minimax rates for heterogeneous causal effect estimation
Minimax rates for heterogeneous causal effect estimationAnnals of Statistics (Ann. Stat.), 2022
Edward H. Kennedy
Sivaraman Balakrishnan
James M. Robins
Larry A. Wasserman
CML
441
45
0
02 Mar 2022
Towards optimal doubly robust estimation of heterogeneous causal effects
Towards optimal doubly robust estimation of heterogeneous causal effectsElectronic Journal of Statistics (EJS), 2020
Edward H. Kennedy
CML
840
455
0
29 Apr 2020
High Dimensional M-Estimation with Missing Outcomes: A Semi-Parametric
  Framework
High Dimensional M-Estimation with Missing Outcomes: A Semi-Parametric Framework
Abhishek Chakrabortty
Jiarui Lu
T. Tony Cai
Hongzhe Li
232
8
0
26 Nov 2019
Group Average Treatment Effects for Observational Studies
Group Average Treatment Effects for Observational Studies
D. Jacob
CML
358
20
0
07 Nov 2019
De-Biased Machine Learning of Global and Local Parameters Using
  Regularized Riesz Representers
De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers
Victor Chernozhukov
Whitney Newey
Rahul Singh
744
106
0
23 Feb 2018
Selective inference for effect modification via the lasso
Selective inference for effect modification via the lasso
Qingyuan Zhao
Dylan S. Small
Ashkan Ertefaie
CML
366
54
0
22 May 2017
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