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. 1507.01524
  4. Cited By
A Complete Generalized Adjustment Criterion

A Complete Generalized Adjustment Criterion

6 July 2015
Emilija Perković
J. Textor
M. Kalisch
Marloes H. Maathuis
    OffRL
    CML
ArXivPDFHTML

Papers citing "A Complete Generalized Adjustment Criterion"

10 / 10 papers shown
Title
Your Assumed DAG is Wrong and Here's How To Deal With It
Kirtan Padh
Zhufeng Li
Cecilia Casolo
Niki Kilbertus
CML
84
0
0
24 Feb 2025
Practically Effective Adjustment Variable Selection in Causal Inference
Practically Effective Adjustment Variable Selection in Causal Inference
Atsushi Noda
Takashi Isozaki
104
0
0
04 Feb 2025
Learning Sparse Causal Models is not NP-hard
Learning Sparse Causal Models is not NP-hard
Tom Claassen
Joris Mooij
Tom Heskes
CML
83
119
0
26 Sep 2013
Structural Intervention Distance (SID) for Evaluating Causal Graphs
Structural Intervention Distance (SID) for Evaluating Causal Graphs
J. Peters
Peter Buhlmann
CML
84
40
0
05 Jun 2013
Causal Inference and Causal Explanation with Background Knowledge
Causal Inference and Causal Explanation with Background Knowledge
Christopher Meek
CML
263
634
0
20 Feb 2013
Order-independent constraint-based causal structure learning
Order-independent constraint-based causal structure learning
Diego Colombo
Marloes H. Maathuis
CML
118
605
0
14 Nov 2012
On the Validity of Covariate Adjustment for Estimating Causal Effects
On the Validity of Covariate Adjustment for Estimating Causal Effects
I. Shpitser
T. VanderWeele
J. M. Robins
CML
92
203
0
15 Mar 2012
Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective
Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective
J. Textor
Maciej Liskiewicz
CML
71
70
0
14 Feb 2012
Learning high-dimensional directed acyclic graphs with latent and
  selection variables
Learning high-dimensional directed acyclic graphs with latent and selection variables
Diego Colombo
Marloes H. Maathuis
M. Kalisch
Thomas S. Richardson
CML
123
466
0
29 Apr 2011
Markov equivalence for ancestral graphs
Markov equivalence for ancestral graphs
R. A. Ali
Thomas S. Richardson
Peter Spirtes
156
112
0
25 Aug 2009
1