ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2006.16789
  4. Cited By
Causality Learning: A New Perspective for Interpretable Machine Learning

Causality Learning: A New Perspective for Interpretable Machine Learning

27 June 2020
Guandong Xu
Tri Dung Duong
Q. Li
S. Liu
Xianzhi Wang
    XAI
    OOD
    CML
ArXivPDFHTML

Papers citing "Causality Learning: A New Perspective for Interpretable Machine Learning"

8 / 8 papers shown
Title
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for
  Tabular Data using Normalizing Flows
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows
Tri Dung Duong
Qian Li
Guandong Xu
OOD
37
7
0
26 Mar 2023
Navigating causal deep learning
Navigating causal deep learning
Jeroen Berrevoets
Krzysztof Kacprzyk
Zhaozhi Qian
M. Schaar
CML
41
2
0
01 Dec 2022
Application of Causal Inference to Analytical Customer Relationship
  Management in Banking and Insurance
Application of Causal Inference to Analytical Customer Relationship Management in Banking and Insurance
Satyam Kumar
V. Ravi
11
1
0
19 Aug 2022
Leveraging Explanations in Interactive Machine Learning: An Overview
Leveraging Explanations in Interactive Machine Learning: An Overview
Stefano Teso
Öznur Alkan
Wolfgang Stammer
Elizabeth M. Daly
XAI
FAtt
LRM
26
62
0
29 Jul 2022
When to intervene? Prescriptive Process Monitoring Under Uncertainty and
  Resource Constraints
When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints
Mahmoud Shoush
Marlon Dumas
32
12
0
15 Jun 2022
Causal Learning for Socially Responsible AI
Causal Learning for Socially Responsible AI
Lu Cheng
Ahmadreza Mosallanezhad
Paras Sheth
Huan Liu
71
13
0
25 Apr 2021
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery
M. Vowels
Necati Cihan Camgöz
Richard Bowden
CML
37
296
0
03 Mar 2021
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
257
3,684
0
28 Feb 2017
1