Inference of Cause and Effect with Unsupervised Inverse Regression

By Eleni Sgouritsa, Dominik Janzing, Philipp Hennig, Bernhard Schölkopf. 2015

80 citations recorded by the source corpus.

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In Causal Inference & Discovery

Citations in the corpus

Group invariance principles for causal generative models (2017, neutral): “The main use of ICM postulates has been the development of several causal inference algorithms for cause-effect pairs (Janzing et al., 2010; Zscheischler et al., 2011; Daniusis et al., 2010; Janzing et al., 2012; Shajarisales et al., 2015; Sgouritsa et al., 2015); however, results in Schölkopf et al”
Telling cause from effect in deterministic linear dynamical systems (2015, methodology): “The idea behind SIC, as well as several new approaches to causal inference (Daniusis et al., 2010; Janzing et al., 2010; Mooij et al., 2010; Zscheischler et al., 2011; Janzing et al., 2012; Sgouritsa et al., 2015), is to rely on the “philosophical” principle that the cause and”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, background): “The former conclusion about the performance of ANM-pHSIC is in line with earlier reports, but the latter conclusion is surprising, considering that good performance of IGCI-slope and IGCI-ent-1sp has been reported on several occasions in earlier work (Daniušis et al., 2010; Mooij et al., 2010; Janzi”
Origo: causal inference by compression (2016, methodology): “those pairs where Origo is most decisive, we see that over the top 8% most decisive pairs it is 75% accurate, yet still 70% accurate for the top 21% pairs, which is comparable with the top-performing causal inference frameworks for continuous real-valued data [16,27,30].”
Semi-supervised interpolation in an anticausal learning scenario (2015, background): “Why should PX contain information about PY |X? Some recent approaches to distinguish cause and effect in causal structure learning (Janzing and Schölkopf, 2010; Daniusis et al., 2010; Janzing et al., 2012; Sgouritsa et al., 2015) were motivated by an informal ‘independence’ postulate stating that Pc”
Cause-Effect Inference by Comparing Regression Errors (2018, methodology): “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].”
Explainable and Interpretable Models in Computer Vision and Machine Learning (2017, unknown): “Other causal inference methods (Sgouritsa et al., 2015) are based on the idea that if X → Y , the marginal probability distribution of the cause P ( X ) is independent of the causal mechanism P ( Y | X ) , hence estimating P ( Y | X ) from P ( X ) should hardly be possible, while estimating P ( X Y…”
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., 201”
A Survey of Learning Causality with Data: Problems and Methods (2018, background): “The fact that SSL can only work in the anti-causal direction can help learn causal relationships [123].”
A Survey of Learning Causality with Data (2018, background): “d outperforms supervised learning in the anti-causal direction whenever P(X)and P(Y|X)are independent. The fact that SSL can only work in the anti-causal direction can help learn causal relationships [123]. 5.2 Domain Adaptation Domain adaptation [18, 34] studies how to adapt machine learning models”
Telling Cause from Effect Using MDL-Based Local and Global Regression (2017, methodology): “To generate Y we first apply either a linear, cubic or reciprocal function on X , with fixed parameters, and add either additive noise using a uniform or Gaussian distribution with t, s ∼ unif(1,max(x)/2) or non-additive noise with N (0, 1)| sin(2πνX)|+N (0, 1)| sin(2π(10ν)X)|/4 according to [24], w”
Learning Causal Structures Using Regression Invariance (2017, result): “Similar assumptions have been considered in other work [3, 30, 14, 13, 29, 23].”
Causal Inference by Stochastic Complexity (2017, methodology): “Altogether we can compute the multinomial stochastic complexity in O(n).”
Learning Sparse Nonparametric DAGs (2019, methodology): “…additive noise models (Hoyer et al., 2009; Peters et al., 2014; Blöbaum et al., 2018; Mooij et al., 2016), post-nonlinear models (Zhang and Hyvärinen, 2009; Zhang et al., 2016) and general nonlinear SEM (Monti et al., 2019; Goudet et al., 2018; Kalainathan et al., 2018; Sgouritsa et al., 2015).”