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Causal effect estimation from observational and interventional data through matrix weighted linear estimators.

K. Kladny, J. von Kügelgen, B. Schölkopf, and M. Muehlebach. UAI, volume 216 of Proceedings of Machine Learning Research, page 1087-1097. PMLR, (2023)

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Sebastian Kügelgen

Benedikt Kügelgen

Kurt Kügelgen

Anke Kügelgen

Hartmut Kügelgen

Other publications of authors with the same name

A Sparsity Principle for Partially Observable Causal Representation Learning.D. Xu, D. Yao, S. Lachapelle, P. Taslakian, J. von Kügelgen, F. Locatello, and S. Magliacane. CoRR, (2024)DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability.C. Eastwood, A. Nicolicioiu, J. von Kügelgen, A. Kekic, F. Träuble, A. Dittadi, and B. Schölkopf. CoRR, (2022)Multi-View Causal Representation Learning with Partial Observability.D. Yao, D. Xu, S. Lachapelle, S. Magliacane, P. Taslakian, G. Martius, J. von Kügelgen, and F. Locatello. CoRR, (2023)Kernel-Based Independence Tests for Causal Structure Learning on Functional Data.F. Laumann, J. von Kügelgen, J. Park, B. Schölkopf, and M. Barahona. Entropy, 25 (12): 1597 (December 2023)Age-stratified Covid-19 case fatality rates (CFRs): different countries and longitudinal.J. von Kügelgen, L. Gresele, and B. Schölkopf. (May 2022)On the Fairness of Causal Algorithmic Recourse.J. von Kügelgen, A. Karimi, U. Bhatt, I. Valera, A. Weller, and B. Schölkopf. AAAI, page 9584-9594. AAAI Press, (2022)Visual Representation Learning Does Not Generalize Strongly Within the Same Domain.L. Schott, J. von Kügelgen, F. Träuble, P. Gehler, C. Russell, M. Bethge, B. Schölkopf, F. Locatello, and W. Brendel. ICLR, OpenReview.net, (2022)Algorithmic recourse under imperfect causal knowledge: a probabilistic approach.A. Karimi, J. von Kügelgen, B. Schölkopf, and I. Valera. CoRR, (2020)Nonparametric Identifiability of Causal Representations from Unknown Interventions.J. von Kügelgen, M. Besserve, W. Liang, L. Gresele, A. Kekic, E. Bareinboim, D. Blei, and B. Schölkopf. CoRR, (2023)A bacterial size law revealed by a coarse-grained model of cell physiology.F. Bertaux, J. von Kügelgen, S. Marguerat, and V. Shahrezaei. PLoS Comput. Biol., (2020)

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Disambiguation of "von Kügelgen, Julius"

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Causal effect estimation from observational and interventional data through matrix weighted linear estimators.

Please choose a person to relate this publication to

Sebastian Kügelgen

Benedikt Kügelgen

Kurt Kügelgen

Anke Kügelgen

Hartmut Kügelgen

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "von Kügelgen, Julius"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Causal effect estimation from observational and interventional data through matrix weighted linear estimators.

Please choose a person to relate this publication to

Sebastian Kügelgen

Benedikt Kügelgen

Kurt Kügelgen

Anke Kügelgen

Hartmut Kügelgen

Other publications of authors with the same name

Disambiguation

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community post
history of this post
URL
DOI
BibTeX
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APA
Chicago
DIN 1505
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Causal effect estimation from observational and interventional data through matrix weighted linear estimators.