Causal reasoning by evaluating the complexity of conditional densities with kernel methods

By Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf. 2008

38 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, background): “A variety of causal discovery methods has been proposed in recent years (Friedman and Nachman, 2000; Kano and Shimizu, 2003; Shimizu et al., 2006; Sun et al., 2006, 2008; Hoyer et al., 2009; Mooij et al., 2009; Zhang and Hyvärinen, 2009; Janzing et al., 2010; Mooij et al., 2010; Daniušis et al.,…”
Causal Inference Using the Algorithmic Markov Condition (2008, background): “In [11] we have proposed the following principle for causal inference: Given a joint distribution of the random variabl es”
On the Identifiability of the Post-Nonlinear Causal Model (2009, background): “It is also related to the idea of causal reasoning based on evaluating the complexity of conditional densities (Sun et al., 2008), since it intrinsically admits a simple expression for the conditional density of the effect given the causes.”
Distinguishing between cause and effect (2008, background): “Recent work (Kano and Shimizu, 2003; Sun et al., 2006; Shimizu et al., 2006; Sun et al., 2008; Hoyer et al., 2009; Janzing and Schölkopf, 2008) suggests that the shape of the joint distribution shows asymmetries between cause and effect, which often indicates the causal direction with some…”
Identifying Cause and Effect on Discrete Data using Additive Noise Models (2010, methodology): “For discrete variables, Sun et al. (2008) propose a method to measure the complexity of causal models via a Hilbert space norm of the logarithm of conditional densities and prefer models that induce smaller Identifying Cause and Effect on Discrete Data using Additive Noise Models norms.”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, background): “A variety of causal discovery methods has been proposed in recent years (Friedman and Nachman, 2000; Kano and Shimizu, 2003; Shimizu et al., 2006; Sun et al., 2006, 2008; Hoyer et al., 2009; Mooij et al., 2009; Zhang and Hyvärinen, 2009; Janzing et al., 2010; Mooij et al., 2010; Daniušis et al.,…”
Relating the thermodynamic arrow of time to the causal arrow (2007, background): “The common part of the rules in [18] and [19] is that one first maximizes the entropy of the cause variable subject to simple constraints, and then the conditional entropy of the effect, given the cause and subject to some other simple constraints.”