Causation, Prediction, and Search, Second Edition

By Peter Spirtes, Clark Glymour, Richard Scheines. 2000

284 citations recorded by the source corpus.

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Citations in the corpus

Causal Markov condition for submodular information measures (2010, background): “At its basis is the following Lemma describing that the CMC on a given set of observations can be derived from the causal Markov condition with respect to an extended causal graph (see Figure 1).”
From hype to reality: data science enabling personalized medicine (2018, background): “Under appropriate assumptions, causal graphical models can be learned from observational data [57 – 59].”
Scientific Explanation (2005, background): “Since that time, philosophers have come to see claims about causal relations as having a rich empirical content that goes far beyond mere regularities and their instantiation (see Spirtes et al. (2000), though the tradition began well before Hempel made his remark, with Reichenbach (1956)).”
The seven tools of causal inference, with reflections on machine learning (2019, background): “where causal queries can be estimated directly from that set.(9,18,24,31) Alternatively, Shimizu et al.”
Bayesian Artificial Intelligence (2004, background): “TETRAD II also recovers the ALARM network to within a few arcs [265], although this is more impressive than the K2 result, since it needed no prior temporal ordering of the variables.”
Granger causality and path diagrams for multivariate time series (2007, methodology): “More precisely, we assume for the underlying process X that X is stable (Pearl, 2000) or faithful (Spirtes et al., 2001) with respect to G, that is, a Grangernoncausality relation holds for X if and only if it can be derived from the associated path diagram G (and the same for contemporaneous correl”
Causal reasoning in a prediction task with hidden causes (2015, methodology): “The theory uses a popular probabilistic framework called causal Bayes nets that permits a precise mathematical formulation of causal reasoning under this distinction (Spirtes and Scheines, 2001; Pearl, 2009; Dawid, 2007).”
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis (2007, methodology): “Under the following conditions, the structure learning algorithm considered will discover a DAG structure equivalent to the DAG structure ofP0 (Spirtes et al. 2000):”
Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs (2013, methodology): “However, assuming e.g. faithfulness of the distribution as in (2), one can identify the observational Markov equivalence class E ( D 0 ) = E ( P obs ) from P obs , see for example Spirtes et al. (2000) or Pearl (2000).”
Guidelines for a graph-theoretic implementation of structural equation modeling (2012, methodology): “At present, the TETRAD software (Spirtes et al. 2000) is perhaps the most complete option.”
Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview (2010, background): “Note that D 0 is similar to the ‘manipulated’ DAG of Spirtes, Glymour and Scheines (2000).”