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Regularized Orthogonal Machine Learning for Nonlinear Semiparametric
  Models
v1v2v3v4v5v6v7v8 (latest)

Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models

13 June 2018
Denis Nekipelov
Vira Semenova
Vasilis Syrgkanis
ArXiv (abs)PDFHTML

Papers citing "Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models"

12 / 12 papers shown
Causal hybrid modeling with double machine learning
Causal hybrid modeling with double machine learning
Kai-Hendrik Cohrs
Gherardo Varando
Nuno Carvalhais
Markus Reichstein
Gustau Camps-Valls
506
20
0
20 Feb 2024
Stable Probability Weighting: Large-Sample and Finite-Sample Estimation
  and Inference Methods for Heterogeneous Causal Effects of Multivalued
  Treatments Under Limited Overlap
Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited OverlapSocial Science Research Network (SSRN), 2023
Ganesh Karapakula
309
0
0
13 Jan 2023
Machine Learning based Framework for Robust Price-Sensitivity Estimation
  with Application to Airline Pricing
Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing
Ravi Kumar
Shahin Boluki
Karl Isler
Jonas Rauch
Darius Walczak
OOD
209
3
0
04 May 2022
Orthogonal Statistical Learning with Self-Concordant Loss
Orthogonal Statistical Learning with Self-Concordant LossAnnual Conference Computational Learning Theory (COLT), 2022
Lang Liu
Carlos Cinelli
Zaïd Harchaoui
318
2
0
30 Apr 2022
Knowledge Distillation as Semiparametric Inference
Knowledge Distillation as Semiparametric InferenceInternational Conference on Learning Representations (ICLR), 2021
Tri Dao
G. Kamath
Vasilis Syrgkanis
Lester W. Mackey
276
37
0
20 Apr 2021
Deep Learning for Individual Heterogeneity
Deep Learning for Individual Heterogeneity
M. Farrell
Tengyuan Liang
S. Misra
BDL
501
17
0
28 Oct 2020
Double/Debiased Machine Learning for Dynamic Treatment Effects via
  g-Estimation
Double/Debiased Machine Learning for Dynamic Treatment Effects via g-EstimationNeural Information Processing Systems (NeurIPS), 2020
Greg Lewis
Vasilis Syrgkanis
CML
531
48
0
17 Feb 2020
Localized Debiased Machine Learning: Efficient Inference on Quantile
  Treatment Effects and Beyond
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and BeyondJournal of machine learning research (JMLR), 2019
Nathan Kallus
Xiaojie Mao
Masatoshi Uehara
443
36
0
30 Dec 2019
Machine Learning Estimation of Heterogeneous Treatment Effects with
  Instruments
Machine Learning Estimation of Heterogeneous Treatment Effects with InstrumentsNeural Information Processing Systems (NeurIPS), 2019
Vasilis Syrgkanis
Victor Lei
Miruna Oprescu
Maggie Hei
Keith Battocchi
Greg Lewis
CML
229
78
0
24 May 2019
Orthogonal Statistical Learning
Orthogonal Statistical Learning
Dylan J. Foster
Vasilis Syrgkanis
779
222
0
25 Jan 2019
Non-Parametric Inference Adaptive to Intrinsic Dimension
Non-Parametric Inference Adaptive to Intrinsic Dimension
Khashayar Khosravi
Greg Lewis
Vasilis Syrgkanis
894
8
0
11 Jan 2019
Fisher-Schultz Lecture: Generic Machine Learning Inference on
  Heterogenous Treatment Effects in Randomized Experiments, with an Application
  to Immunization in India
Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India
Victor Chernozhukov
Mert Demirer
E. Duflo
Iván Fernández-Val
CMLFedML
684
11
0
13 Dec 2017
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