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. 1707.02171
  4. Cited By
Interpreting and using CPDAGs with background knowledge
v1v2 (latest)

Interpreting and using CPDAGs with background knowledge

7 July 2017
Emilija Perković
M. Kalisch
Marloes H. Maathuis
ArXiv (abs)PDFHTML

Papers citing "Interpreting and using CPDAGs with background knowledge"

13 / 13 papers shown
Title
Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
Zheng Li
Feng Xie
Xichen Guo
Yan Zeng
Hao Zhang
Zhi Geng
CML
196
0
0
25 Nov 2024
Data-Driven Causal Effect Estimation Based on Graphical Causal
  Modelling: A Survey
Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey
Debo Cheng
Jiuyong Li
Lin Liu
Jixue Liu
T. Le
CML
84
32
0
20 Aug 2022
On the Representation of Causal Background Knowledge and its
  Applications in Causal Inference
On the Representation of Causal Background Knowledge and its Applications in Causal Inference
Zhuangyan Fang
Ruiqi Zhao
Yue Liu
Yangbo He
72
4
0
10 Jul 2022
Counterfactual Fairness with Partially Known Causal Graph
Counterfactual Fairness with Partially Known Causal Graph
Aoqi Zuo
Susan Wei
Tongliang Liu
Bo Han
Kun Zhang
Biwei Huang
OODFaML
65
19
0
27 May 2022
Typing assumptions improve identification in causal discovery
Typing assumptions improve identification in causal discovery
P. Brouillard
Perouz Taslakian
Alexandre Lacoste
Sébastien Lachapelle
Alexandre Drouin
CML
76
13
0
22 Jul 2021
Minimal enumeration of all possible total effects in a Markov
  equivalence class
Minimal enumeration of all possible total effects in a Markov equivalence class
F. R. Guo
Emilija Perković
CML
38
17
0
16 Oct 2020
Efficient least squares for estimating total effects under linearity and
  causal sufficiency
Efficient least squares for estimating total effects under linearity and causal sufficiency
By F. Richard Guo
Emilija Perković
CML
81
13
0
08 Aug 2020
On efficient adjustment in causal graphs
On efficient adjustment in causal graphs
Jan-Jelle Witte
Leonard Henckel
Marloes H. Maathuis
Vanessa Didelez
CML
70
70
0
17 Feb 2020
Identifying causal effects in maximally oriented partially directed
  acyclic graphs
Identifying causal effects in maximally oriented partially directed acyclic graphs
Emilija Perković
CML
59
39
0
07 Oct 2019
Graphical Criteria for Efficient Total Effect Estimation via Adjustment
  in Causal Linear Models
Graphical Criteria for Efficient Total Effect Estimation via Adjustment in Causal Linear Models
Leonard Henckel
Emilija Perković
Marloes H. Maathuis
CML
98
108
0
04 Jul 2019
Separators and Adjustment Sets in Causal Graphs: Complete Criteria and
  an Algorithmic Framework
Separators and Adjustment Sets in Causal Graphs: Complete Criteria and an Algorithmic Framework
Benito van der Zander
Maciej Liskiewicz
J. Textor
CML
62
37
0
28 Feb 2018
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees
AmirEmad Ghassami
Saber Salehkaleybar
Negar Kiyavash
Kun Zhang
CML
74
22
0
05 Feb 2018
Complete Graphical Characterization and Construction of Adjustment Sets
  in Markov Equivalence Classes of Ancestral Graphs
Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs
Emilija Perković
J. Textor
M. Kalisch
Marloes H. Maathuis
OffRL
86
149
0
22 Jun 2016
1