Distinguishing cause from effect using observational data: methods and
benchmarks

Distinguishing cause from effect using observational data: methods and benchmarks

11 December 2014

Joris M. Mooij

Dominik Janzing

Jakob Zscheischler

Bernhard Schölkopf

Papers citing "Distinguishing cause from effect using observational data: methods and benchmarks"

8 / 8 papers shown

Title
Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery Rebecca Herman Jonas Wahl Urmi Ninad Jakob Runge 79 1 0 21 Mar 2025
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models Ruta Binkyte Ivaxi Sheth Zhijing Jin Mohammad Havaei Bernhard Schölkopf Mario Fritz 382 1 0 28 Feb 2025
Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm Mathieu Chevalley Patrick Schwab Arash Mehrjou 83 1 0 28 May 2024
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach Masayuki Takayama Tadahisa Okuda Thong Pham T. Ikenoue Shingo Fukuma Shohei Shimizu Akiyoshi Sannai 107 18 0 02 Feb 2024
Structural restrictions in local causal discovery: identifying direct causes of a target variable Juraj Bodik V. Chavez-Demoulin CML 52 1 0 29 Jul 2023
Causal Discovery with Continuous Additive Noise Models Jonas Peters Joris Mooij Dominik Janzing Bernhard Schölkopf CML 84 563 0 26 Sep 2013
On Causal and Anticausal Learning Bernhard Schölkopf Dominik Janzing J. Peters Eleni Sgouritsa Kun Zhang Joris Mooij CML 74 604 0 27 Jun 2012
Hilbert space embeddings and metrics on probability measures Bharath K. Sriperumbudur Arthur Gretton Kenji Fukumizu Bernhard Schölkopf Gert R. G. Lanckriet 154 741 0 30 Jul 2009