Causation, Prediction, and Search, 2nd Edition

By Peter Spirtes, Clark Glymour, Richard Scheines. 2001

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Citations in the corpus

Invariant Causal Prediction for Sequential Data (2017, methodology): “Many procedures in this field only use observational data (e.g., Spirtes, Glymour, and Scheines 2000; Chickering 2002; Shimizu et al. 2006; Janzing et al. 2012; van de Geer and Bühlmann 2013; Peters et al. 2014; Bühlmann, Peters, and Ernest 2014).”
CAM: Causal Additive Models, high-dimensional order search and penalized regression (2013, methodology): “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].”
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution (2018, background): “Systematic searches have been developed which, in certain circumstances, can prune the set of compatible models signiﬁcantly to the point where causal queries can be estimated directly from that set (Spirtes et al., 2000; Pearl, 2000; Peters et al., 2017).”
Graphical models for marked point processes based on local independence (2007, background): “…intelligence (Pearl, 1988; Cowell et al., 1999; Jordan, 1999) and hierarchical Bayesian modelling (see the BUGS project at http://www. mrc-bsu.cam.ac.uk/bugs/welcome.shtml), causal reasoning (Pearl, 2000; Spirtes et al., 2000), as well as sociological, biomedical and econometric applications.”
A Linear Non-Gaussian Acyclic Model for Causal Discovery (2006, methodology): “Existing discovery algorithms (Spirtes et al., 2000; Pearl, 2000) generally work in one of two settings.”
Estimating the effect of joint interventions from observational data in sparse high-dimensional settings (2014, methodology): “Then for some v > 0, sup i pn,r≤mn Var(Xnpn |Xni,PAi (Gnr)) Var(Xni |PAi (Gnr)) ≤ v. Assumptions (A1)–(A4) are required for consistency of the PC-algorithm [14].”
Transportability across studies: A formal approach (2011, background): “The advent of causal diagrams (Pearl, 1995; Greenland et al., 1999; Spirtes et al., 2000; Pearl, 2009) provides such a language and renders the formalization of transportability possible.”
Likelihood ratio tests and singularities (2007, background): “Such tests constitute edge exclusion tests in graphical models with one hidden variable; compare, for example, [30].”
A Minimum Relative Entropy Principle for Learning and Acting (2008, background): “Especially over the last decade, significant progress has been made towards the formal understanding of causation (Shafer, 1996; Pearl, 2000; Spirtes et al., 2000; Dawid, 2010).”
The Similarity of Causal Inference in Experimental and Non‐experimental Studies (2005, methodology): “Any pair of variables that are not adjacent after the adjacency phase are not adjacent in any causal graph in the final equivalence class (Spirtes, et al. 2000).”
Adjacency-Faithfulness and Conservative Causal Inference (2006, methodology): “In-deed a predecessor of the PC algorithm, called the SGS algorithm (Spirtes et al. 2000), is almost correct.”
Network analysis: a brief overview and tutorial (2018, background): “Spirtes, Glymour, and Scheines (2000) developed the PC algorithm, which can be used to examine networks to find candidate causal structures that may have generated the observed patterns of relations present.”
A survey of Bayesian Network structure learning (2021, background): “Spirtes et al. (1993; 2000) described the Fast Causal Inference (FCI) algorithm for structure learning without assuming causal sufficiency, though the causal Markov and causal faithfulness conditions are still assumed.”
Inferring causal networks from observations and interventions (2003, methodology): “The Markov condition directly forms the basis for many algorithms for causal inference (Glymour & Cooper, 1999; Pearl, 2000; Spirtes et al., 2000).”