On Causal Discovery with Cyclic Additive Noise Models

By Joris M. Mooij, Dominik Janzing, Tom M. Heskes, Bernhard Schölkopf. 2011

103 citations recorded by the source corpus.

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In Causal Inference & Discovery

Citations in the corpus

Toward Causal Representation Learning (2021, background): “[229] for linear functions, and it can be generalized to include nonlinear rescalings [279], loops [174], confounders [123], and multivariable settings [189].”
Towards Causal Representation Learning (2021, background): “[237, 188, 229, 113, 174, 187, 139, 17, 246, 277, 175, 123, 186, 176, 36, 82, 161], learning causal relations frequently requires collecting data from multiple environments, or the ability to perform interventions [251].”
Causality for Machine Learning (2019, background): “It generalizes results of Shimizu et al. (2006) for linear functions, and it can be generalized to include nonlinear rescalings (Zhang and Hyvärinen, 2009), loops (Mooij et al., 2011), confounders (Janzing et al., 2009), and multi-variable settings (Peters et al., 2011).”
Causal inference by using invariant prediction: identification and confidence intervals (2015, background): “We do not touch on the framework of feedback models [Lauritzen and Richardson, 2002, Mooij et al., 2011, Hyttinen et al., 2012, cf.], and it is an open question whether our concept of invariance would provide a useful key for such models as well.”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, background): “Without having access to the interventional distributions, this symmetry prevents us from drawing any conclusions regarding the direction of the causal relationship between X and Y if we only have access to the observational distribution PX,Y .”
CAM: Causal Additive Models, high-dimensional order search and penalized regression (2013, background): “Finally, it would be very interesting to extend (sparse) permutation search to (possibly nonidentifiable) models with hidden variables [7, 11, 23, 34] or with graph structures allowing for cycles [19, 20, 30, 33].”
UvA-DARE ( Digital Academic Repository ) Causal Consistency of Structural Equation Models (2017, unknown): “12 This interpretation corresponds to the assumption that the noise in the dynamical model is constant through time, and is used by e.g. [10, 12, 14, 15] and [13] to meaningfully interpret cyclic SEMs.”
From Random Differential Equations to Structural Causal Models: the stochastic case (2018, background): “One of the advantages of SCMs over other causal modeling tools is that they have the ability to deal with cyclic causal relationships (Spirtes, 1995; Hyttinen et al., 2012; Mooij et al., 2011; Forré and Mooij, 2017; Bongers et al., 2016).”
Causal feature learning: an overview (2017, unknown): “Like the majority of work in causal inference and discovery—notable exceptions being (Richardson 1996; Mooij et al. 2011; Lacerda et al. 2012; Hyttinen et al. 2012, 2014)—CFL assumes that the microvariable system is acyclic: in our toy example, hue has causal influence on eda, but we assumed (quite…”
UvA-DARE ( Digital Academic Repository ) Causal Discovery with Continuous Additive Noise Models (2014, unknown): “, if both X → Y and Y → X), at least when assuming noise and input distributions to be Gaussian (Mooij et al., 2011).”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, background): “This leads us to Algorithm 1 as a general scheme for identifying the direction of the ANM.”
High-Dimensional Statistics with a View Toward Applications in Biology (2014, background): “Some relaxations of these conditions have been worked out: the FCI algorithm allows for hidden variables (Spirtes et al., 2000; Colombo et al., 2012) while graphs with directed cycles are considered in Spirtes (1995), Richardson (1996) and Mooij et al. (2011).”
From Dependence to Causation (2016, background): “For more details, consult (Mooij et al., 2011).”