OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!
If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.
Requested Article:
Long Story Short: Omitted Variable Bias in Causal Machine Learning
Victor Chernozhukov, Carlos Cinelli, Whitney K. Newey, et al.
(2022)
Open Access | Times Cited: 9
Victor Chernozhukov, Carlos Cinelli, Whitney K. Newey, et al.
(2022)
Open Access | Times Cited: 9
Showing 9 citing articles:
AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation
Shalmali Joshi, Iñigo Urteaga, Wouter A. C. van Amsterdam, et al.
Journal of the American Medical Informatics Association (2025)
Open Access
Shalmali Joshi, Iñigo Urteaga, Wouter A. C. van Amsterdam, et al.
Journal of the American Medical Informatics Association (2025)
Open Access
An Omitted Variable Bias Framework for Sensitivity Analysis of Instrumental Variables
Carlos Cinelli, Chad Hazlett
SSRN Electronic Journal (2022)
Closed Access | Times Cited: 26
Carlos Cinelli, Chad Hazlett
SSRN Electronic Journal (2022)
Closed Access | Times Cited: 26
Double machine learning and automated confounder selection: A cautionary tale
Paul Hünermund, Beyers Louw, Itamar Caspi
Journal of Causal Inference (2023) Vol. 11, Iss. 1
Open Access | Times Cited: 13
Paul Hünermund, Beyers Louw, Itamar Caspi
Journal of Causal Inference (2023) Vol. 11, Iss. 1
Open Access | Times Cited: 13
Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding
Jacob Dorn, Kevin Guo, Nathan Kallus
Journal of the American Statistical Association (2024), pp. 1-12
Open Access | Times Cited: 1
Jacob Dorn, Kevin Guo, Nathan Kallus
Journal of the American Statistical Association (2024), pp. 1-12
Open Access | Times Cited: 1
Quantitative probing: Validating causal models with quantitative domain knowledge
Daniel Grünbaum, Maike Lorena Stern, Elmar W. Lang
Journal of Causal Inference (2023) Vol. 11, Iss. 1
Open Access | Times Cited: 3
Daniel Grünbaum, Maike Lorena Stern, Elmar W. Lang
Journal of Causal Inference (2023) Vol. 11, Iss. 1
Open Access | Times Cited: 3
Sensitivity analysis for causal effects with generalized linear models
Arvid Sjölander, Erin E. Gabriel, Iuliana Ciocănea‐Teodorescu
Journal of Causal Inference (2022) Vol. 10, Iss. 1, pp. 441-479
Open Access | Times Cited: 4
Arvid Sjölander, Erin E. Gabriel, Iuliana Ciocănea‐Teodorescu
Journal of Causal Inference (2022) Vol. 10, Iss. 1, pp. 441-479
Open Access | Times Cited: 4
Digging Up Threats to Validity: A Data Marshalling Approach to Sensitivity Analysis
Anna Zeng, Mike Cafarella
(2024)
Closed Access
Anna Zeng, Mike Cafarella
(2024)
Closed Access
Structural causal modeling and STPA for the risk analysis of a rail system powered by H2 fuel
Luca Riccardi, Michele Compare, Roberto Mascherona, et al.
Reliability Engineering & System Safety (2024), pp. 110758-110758
Closed Access
Luca Riccardi, Michele Compare, Roberto Mascherona, et al.
Reliability Engineering & System Safety (2024), pp. 110758-110758
Closed Access
Accelerating Causal Algorithms for Industrial-scale Data: A Distributed Computing Approach with Ray Framework
V. Verma, Vinod Reddy, Jaiprakash Ravi
(2023), pp. 1-6
Open Access
V. Verma, Vinod Reddy, Jaiprakash Ravi
(2023), pp. 1-6
Open Access
