Distinguishing between cause and effect

By Joris M. Mooij, Dominik Janzing. 2008

40 citations recorded by the source corpus.

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Citations in the corpus

Inferring deterministic causal relations (2010, methodology): “To encourage the development of novel methods for this challenging problem, the NIPS 2008 workshop Causality - Objectives and Assessment contained CauseEffectPairs as one of its tasks [Mooij and Janzing, 2010].”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, methodology): “In practice, we usually do not have the density p(x, y), but rather a finite sample of it.”
Causal Inference Using the Algorithmic Markov Condition (2008, background): “In particular, deciding whether X causesY or Y causes X for just two observed variables is a challenging task for novel inference rules [9] since it is unsolvable for independence-based methods.”
On the Identifiability of the Post-Nonlinear Causal Model (2009, methodology): “Cambridge, MA: MIT Press.”
Telling cause from effect based on high-dimensional observations (2009, methodology): “The method of [8] is able to infer causal directions among realvalued variables if every effect is a (possibly non-linear) function of its causes up to an additive noise term that is independent of the causes.”
Justifying Additive Noise Model-Based Causal Discovery via Algorithmic Information Theory (2009, methodology): “Here we focus on a method [6, 14, 13] that yielded quite encouraging results on real data sets and show that it also can be justified via algorithmic information theory.”
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).”
Information-geometric approach to inferring causal directions (2012, methodology): “Real-world data: Cause–effect pairs We have also evaluated the IGCI method on real-world data, namely on the extended version of the “Cause–effect pairs” dataset described in [1].”
UvA-DARE ( Digital Academic Repository ) Causal Discovery with Continuous Additive Noise Models (2014, unknown): “Under certain assumptions such as stable equilibria, one can derive an SEM that describes how the equilibrium states of such a dynamical system will react to physical interventions on the observables involved (Mooij et al., 2013). We do not deal with these issues in the present paper but take the SE”
Score-based causal learning in additive noise models (2013, methodology): “Mooji and Janzing [2010] describe 8 such pairs and compare several methods that were submitted as part of the Causality Pot-Luck Challenge.”
Probabilistic latent variable models for distinguishing between cause and effect (2010, background): “The challenge of inferring whether X causes Y (“X → Y ”) or vice versa (“Y → X”) from joint observations of the pair (X,Y ) has recently attracted increasing interest [1, 2, 3, 4, 5, 6, 7, 8].”
Distinguishing Cause and Effect via Second Order Exponential Models (2009, background): “Most examples of the data base “cause eﬀect pairs” in the NIPS 2008 causality competition [27] are of this type, except for the examples with “altitude”.”