Papers citing 'Structural restrictions in local causal discovery: identifying direct causes of a target variable'

Title
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk Ilia Azizi Juraj Bodik Jakob Heiss Bin Yu UQCV UD 362 2 0 10 Jul 2025
Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence TestingNeural Information Processing Systems (NeurIPS), 2023 Xiangyu Sun Oliver Schulte CML 364 6 0 26 Jan 2023
On the Identifiability and Estimation of Causal Location-Scale Noise ModelsInternational Conference on Machine Learning (ICML), 2022 Alexander Immer Christoph Schultheiss Julia E. Vogt Bernhard Schölkopf Peter Buhlmann Alexander Marx CML 261 50 0 13 Oct 2022
Identifying Patient-Specific Root Causes with the Heteroscedastic Noise ModelJournal of Computer Science (JCS), 2022 Eric V. Strobl Thomas A. Lasko CML 365 42 0 25 May 2022
Causality in extremes of time series Juraj Bodik Z. Pawlas Milan Paluš AI4TS 204 11 0 20 Dec 2021
Efficient Bayesian network structure learning via local Markov boundary searchNeural Information Processing Systems (NeurIPS), 2021 Ming Gao Bryon Aragam 368 19 0 12 Oct 2021
Causal Autoregressive Flows Ilyes Khemakhem R. Monti R. Leech Aapo Hyvarinen CML OOD AI4CE 333 126 0 04 Nov 2020
A Critical View of the Structural Causal Model Tomer Galanti Ofir Nabati Lior Wolf CML 194 10 0 23 Feb 2020
On Causal Discovery with Equal Variance Assumption Wenyu Chen Mathias Drton Y Samuel Wang CML 355 96 0 09 Jul 2018
Telling Cause from Effect using MDL-based Local and Global Regression Alexander Marx Jilles Vreeken CML 129 68 0 26 Sep 2017
Invariant Causal Prediction for Sequential Data Niklas Pfister Peter Buhlmann J. Peters OOD 211 133 0 25 Jun 2017
Learning Quadratic Variance Function (QVF) DAG models via OverDispersion Scoring (ODS) G. Park Garvesh Raskutti CML 221 47 0 28 Apr 2017
Kernel-based Tests for Joint Independence Niklas Pfister Peter Buhlmann Bernhard Schölkopf J. Peters 320 197 0 01 Mar 2016
Causal inference using invariant prediction: identification and confidence intervals J. Peters Peter Buhlmann N. Meinshausen OOD 587 1,046 0 06 Jan 2015
Distinguishing cause from effect using observational data: methods and benchmarksJournal of machine learning research (JMLR), 2014 Joris M. Mooij J. Peters Dominik Janzing Jakob Zscheischler Bernhard Schölkopf CML 278 140 0 11 Dec 2014
Score-based Causal Learning in Additive Noise Models Christopher Nowzohour Peter Buhlmann CML 225 35 0 25 Nov 2013
Causal Discovery with Continuous Additive Noise Models Jonas Peters Joris Mooij Dominik Janzing Bernhard Schölkopf CML 407 619 0 26 Sep 2013
On the Equivalence of Causal Models Thomas Verma Judea Pearl 205 27 0 27 Mar 2013
Causal Inference and Causal Explanation with Background KnowledgeConference on Uncertainty in Artificial Intelligence (UAI), 1995 Christopher Meek CML 404 669 0 20 Feb 2013
Identifiability of Gaussian structural equation models with equal error variances J. Peters Peter Buhlmann CML 497 368 0 11 May 2012
On the Identifiability of the Post-Nonlinear Causal ModelConference on Uncertainty in Artificial Intelligence (UAI), 2009 Kun Zhang Aapo Hyvarinen CML 405 608 0 09 May 2012
Causal inference using the algorithmic Markov conditionIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2008 Dominik Janzing Bernhard Schölkopf CML 387 329 0 23 Apr 2008