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Adversarial De-confounding in Individualised Treatment Effects
  Estimation
v1v2v3 (latest)

Adversarial De-confounding in Individualised Treatment Effects Estimation

International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
19 October 2022
Vinod Kumar Chauhan
Soheila Molaei
Marzia Hoque Tania
Anshul Thakur
T. Zhu
David Clifton
    CML
ArXiv (abs)PDFHTML

Papers citing "Adversarial De-confounding in Individualised Treatment Effects Estimation"

5 / 5 papers shown
Title
Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE
Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE
Lokesh Nagalapatti
Pranava Singhal
Avishek Ghosh
Sunita Sarawagi
CML
227
0
0
07 Feb 2025
Orthogonal Representation Learning for Estimating Causal Quantities
Orthogonal Representation Learning for Estimating Causal Quantities
Valentyn Melnychuk
Dennis Frauen
Jonas Schweisthal
Stefan Feuerriegel
CMLOODBDL
402
5
0
06 Feb 2025
Sample Selection Bias in Machine Learning for Healthcare
Sample Selection Bias in Machine Learning for Healthcare
V. Chauhan
Lei A. Clifton
Achille Salaün
Huiqi Yvonne Lu
Kim Branson
Patrick Schwab
Gaurav Nigam
David Clifton
304
3
0
13 May 2024
HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task
  Switching Network for MRI Reconstruction
HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstruction
Sriprabha Ramanarayanan
Mohammad Al Fahim
R. G. S.
Amrit Kumar Jethi
Keerthi Ram
M. Sivaprakasam
156
2
0
09 Aug 2023
HCR-Net: A deep learning based script independent handwritten character
  recognition network
HCR-Net: A deep learning based script independent handwritten character recognition network
Vinod Kumar Chauhan
Sukhdeep Singh
Anand Sharma
222
39
0
15 Aug 2021
1