Causal Inference by Choosing Graphs with Most Plausible Markov Kernels

By Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf. 2006

72 citations recorded by the source corpus.

Subfields: Graph Learning, Causal Inference & Discovery

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In Causal Inference & Discovery

Citations in the corpus

Toward Causal Representation Learning (2021, background): “On the other hand, although methods for learning causal structure from observations exist [18], [37], [83], [113], [123], [139], [161], [174]–[176], [188]–[190], [229], [237], [246], [279], learning causal relations frequently requires collecting data from multiple environments or the ability to per”
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): “…X,Y generated by an additive noise model, one cannot fit an additive noise model in the opposite direction (i.e., with the roles of X and Y interchanged) (Hoyer et al., 2009; Mooij et al., 2009; Peters et al., 2014; Kpotufe et al., 2014; Bauer et al., 2016), cf. also the work of Sun et al. (2006).”
Detecting non-causal artifacts in multivariate linear regression models (2018, background): “…assumption Spirtes et al. (1993); Pearl (2000), researchers have also stated assumptions that admit causal inference when no conditional statistical inde-pendences hold, e.g., Kano and Shimizu (2003); Sun et al. (2006); Hoyer et al. (2009); Zhang and Hyvärinen (2009); Blöbaum et al. (2017).”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, background): “Some approaches, instead, define certain classes of “simple” conditionals, e.g., Additive Noise Models (Hoyer et al., 2009) and second-order exponential models (Sun et al., 2006; Janzing et al., 2009), and infer X to be the cause of Y whenever PY”
Causal Inference Using the Algorithmic Markov Condition (2008, background): “Causal inference from statistical data has attracted increasing interest in the past decade.”
Testing whether linear equations are causal: A free probability theory approach (2011, background): “In particular one can infer – with some reliability – whether X causes Y or Y causes X, given that either statement is true (e.g. Friedman and Nachman (2000); Kano and Shimizu (2003); Sun et al. (2006); Hoyer et al. (2009); Daniušis et al. (2010); Mooij et al. (2009)).”
Causal Inference on Discrete Data Using Additive Noise Models (2009, methodology): “For this data set the method proposed by (Sun et al., 2006) returns a slightly higher likelihood for the true causal directions than for the false directions, but this difference is so small, that the algorithm does not consider it to be significant.”
Causal reasoning by evaluating the complexity of conditional densities with kernel methods (2008, background): “This has been proposed in a conference paper [7] and will be elaborated in the present paper.”
Cause-Effect Inference by Comparing Regression Errors (2018, background): “In particular, the identification of the causal relation between only two observed variables is a challenging task [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15].”
Distinguishing Cause and Effect via Second Order Exponential Models (2009, methodology): “Here we describe a method (ﬁrst proposed in our conference paper [13]) that can deal with combinations of discrete and continuous variables, it even beneﬁts from such a combination.”
Nonlinear causal discovery with additive noise models (2008, methodology): “All five other DAGs for which the true DAG is a subgraph were also consistent with the data. w x y”
Probabilistic latent variable models for distinguishing between cause and effect (2010, methodology): “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].”
Identifying Cause and Effect on Discrete Data using Additive Noise Models (2010, methodology): “Sun et al. (2006); Janzing et al. (2009b) fit joint distributions of cause and effect with conditional densities whose logarithm is a second order polynomial (up to the log-partition function) and show that this often makes causal directions identifiable when some or all variables are discrete.”
Relating the thermodynamic arrow of time to the causal arrow (2007, background): “For this case it was recently proposed that one can plausibly identify C as the cause, if the probability distributions P (E|C) and P (C) are in a certain sense simpler than P (C|E) and P (E), respectively [18].”
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…”
Estimating Causal Direction and Confounding of Two Discrete Variables (2016, background): “Our approach is most similar in spirit to the work of Sun et al. (2006). Sun puts an explicit Bayesian prior on what a likely causal system is: if X causes Y , then the conditionals p(Y | X = x) are less complex than the reverse conditionals P (X | Y = y), where complexity is measured by the Hilbert”
Causal Feature Selection (2007, background): “Distributional hints of that sort are used in causal discovery algorithms recently developed [33].”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, background): “Some approaches, instead, define certain classes of “simple” conditionals, e.g., Additive Noise Models (Hoyer et al., 2009) and second-order exponential models (Sun et al., 2006; Janzing et al., 2009), and infer X to be the cause of Y whenever PY”