Causality : Objectives and Assessment

By Isabelle M Guyon, Dominik Janzing, Bernhard Schölkopf. 2010

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In Causal Inference & Discovery

Citations in the corpus

Causality for Machine Learning (2019, background): “Although a few activities relating to causality had been present at the conference during the years before, in particular a workshop in 2008 (Guyon et al., 2010), it is probably fair to say that the Posner lecture helped pave the way for causality to enter the machine learning mainstream.”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, methodology): “…in the Causality Challenge #2: Pot-Luck competition (Mooij and Janzing, 2010) that was performed as part of the NIPS 2008 Workshop on Causality (Guyon et al., 2010).24 Here we describe version 1.0 of the CauseEffectPairs benchmark, which consists of 100 “cause-effect pairs”, each one…”
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data (2019, background): “Other sources of interventional and empirical data include results of advertising campaigns [Sun et al., 2015] and clinical studies [McDonald et al., 1992], as well as multiple challenges organized for machine learning conferences [Guyon et al., 2008, 2010].”
Model Selection: Beyond the Bayesian/Frequentist Divide (2010, methodology): “Univariate filters were also widely used in the KDD cup 2009, which involved classification tasks on a very large database, to cut down computations (Guyon et al., 2009b).”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, methodology): “It is an extension of the collection of the eight data sets that formed the “CauseEffectPairs” task in the Causality Challenge #2: Pot-Luck competition (Mooij and Janzing, 2010) which was performed as part of the NIPS 2008 Workshop on Causality (Guyon et al., 2010).”