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Estimating Categorical Counterfactuals via Deep Twin Networks

Estimating Categorical Counterfactuals via Deep Twin Networks

4 September 2021
Athanasios Vlontzos
Bernhard Kainz
Ciarán M. Gilligan-Lee
    OOD
    CML
    BDL
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Papers citing "Estimating Categorical Counterfactuals via Deep Twin Networks"

12 / 12 papers shown
Title
Local Interference: Removing Interference Bias in Semi-Parametric Causal Models
Local Interference: Removing Interference Bias in Semi-Parametric Causal Models
Michael O'Riordan
Ciarán M. Gilligan-Lee
CML
41
0
0
24 Mar 2025
Evaluation of Large Language Models via Coupled Token Generation
Evaluation of Large Language Models via Coupled Token Generation
N. C. Benz
Stratis Tsirtsis
Eleni Straitouri
Ivi Chatzi
Ander Artola Velasco
Suhas Thejaswi
Manuel Gomez Rodriguez
46
0
0
03 Feb 2025
Contrastive representations of high-dimensional, structured treatments
Contrastive representations of high-dimensional, structured treatments
Oriol Corcoll Andreu
Athanasios Vlontzos
Michael O'Riordan
Ciarán M. Gilligan-Lee
CML
66
1
0
28 Nov 2024
Counterfactual Generative Modeling with Variational Causal Inference
Counterfactual Generative Modeling with Variational Causal Inference
Yulun Wu
Louie McConnell
Claudia Iriondo
CML
BDL
24
0
0
16 Oct 2024
Teleporter Theory: A General and Simple Approach for Modeling
  Cross-World Counterfactual Causality
Teleporter Theory: A General and Simple Approach for Modeling Cross-World Counterfactual Causality
Jiangmeng Li
Bin Qin
Qirui Ji
Yi Li
Wenwen Qiang
Jianwen Cao
Fanjiang Xu
44
0
0
17 Jun 2024
Benchmarking Counterfactual Image Generation
Benchmarking Counterfactual Image Generation
Thomas Melistas
Nikos Spyrou
Nefeli Gkouti
Pedro Sanchez
Athanasios Vlontzos
Yannis Panagakis
G. Papanastasiou
Sotirios A. Tsaftaris
EGVM
CML
41
7
0
29 Mar 2024
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection
  in Industrial Time Series: Methods, Applications, and Directions
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions
Peng Yan
Ahmed Abdulkadir
Paul-Philipp Luley
Matthias Rosenthal
Gerrit A. Schatte
Benjamin Grewe
Thilo Stadelmann
AI4TS
34
57
0
11 Jul 2023
Partial Counterfactual Identification of Continuous Outcomes with a
  Curvature Sensitivity Model
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
Valentyn Melnychuk
Dennis Frauen
Stefan Feuerriegel
13
11
0
02 Jun 2023
Counterfactual (Non-)identifiability of Learned Structural Causal Models
Counterfactual (Non-)identifiability of Learned Structural Causal Models
Arash Nasr-Esfahany
Emre Kıcıman
24
11
0
22 Jan 2023
Disentangling Causal Effects from Sets of Interventions in the Presence
  of Unobserved Confounders
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders
Olivier Jeunen
Ciarán M. Gilligan-Lee
Rishabh Mehrotra
M. Lalmas
CML
34
12
0
11 Oct 2022
Causal Machine Learning for Healthcare and Precision Medicine
Causal Machine Learning for Healthcare and Precision Medicine
Pedro Sanchez
J. Voisey
Tian Xia
Hannah I. Watson
Alison Q. OÑeil
Sotirios A. Tsaftaris
OOD
CML
39
108
0
23 May 2022
Learning Representations for Counterfactual Inference
Learning Representations for Counterfactual Inference
Fredrik D. Johansson
Uri Shalit
David Sontag
CML
OOD
BDL
215
719
0
12 May 2016
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