Causal Inference from Graphical Models

By Steffen L. Lauritzen. 2001

250 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Causal inference in statistics: An overview (2009, background): “, 1999; Glymour and Greenland, 2008), statisticians (Cox and Wermuth, 2004; Lauritzen, 2001), and social scientists (Morgan and Winship, 2007), its potentials as a comprehensive theory of causation are yet to be fully utilized.”
Probabilistic Soft Interventions in Conditional Gaussian Networks (2005, background): “Hard interventions can be modeled by introducing a policy variable as an additional parent node of the variable at which the intervention is occuring (Pearl, 2000; Spirtes et al., 2000; Lauritzen, 2000).”
Granger causality and path diagrams for multivariate time series (2007, background): “…of graphical models for the analysis of the relationships among variables in multivariate data2 has stimulated new interest in such graphical representations and has led to concepts for a graph-theoretic analysis of causality (e.g., Pearl, 1995, 2000; Spirtes et al., 2001; Lauritzen, 2001).”
Estimating high-dimensional intervention effects from observational data (2008, background): “There is a large existing literature on estimating the equivalence class of DAGs (e.g., [2, 3, 4, 12, 14, 30, 31] and [33]), and there is also a large literature on estimating causal eﬀects when a DAG is given (e.g., [18, 19, 23, 24] and [25]).”
Graphical causal models and VARs: an empirical assessment of the real business cycles hypothesis (2008, background): “…is referred to Appendix A for a definition of graphs and DAGs— have proved to be useful to represent causal hypotheses and to encode independence and conditional independence constraints implied by those hypotheses (Pearl 2000; Spirtes et al. 2000; Lauritzen 2001; Lauritzen and Richardson 2002).”
Causality: a Statistical View (2004, methodology): “Lauritzen (2000) has placed Pearl’s work in the context of the theory of graphical models in the form given in his book (Lauritzen, 1996).”
Statistics and causal inference: A review (2003, unknown): “See Lauritzen (1999).”
Causal Bounds and Observable Constraints for Non-deterministic Models (2012, methodology): “The notation ‘|| ’ (Lauritzen, 2001) is used for intervention conditioning and is equivalent to the ‘do(·)’ notation (Goldszmidt and Pearl, 1992) and the ‘Pman(·)’ notation (Spirtes et al., 1993).”
Statistical Causality from a Decision-Theoretic Perspective (2014, methodology): “Caution: A Pearlian DAG model, or its augmented DAG equivalent, is justified only to the extent that it models the actual the behaviour of the world in the setting to which it is intended to apply.”
Causal Inference of Ambiguous Manipulations (2004, methodology): “For a set of variables V ⊇ X, a manipulation Man(P’(X)) transforms a distribution P(V) into a manipulated distribution over V denoted as P(V||P’(X)), where the double bar ( Lauritzen 2001 ) denotes that the manipulation Man(P’(X)) has been performed.”
Causal analysis with Chain Event Graphs (2010, methodology): “Using Lauritzen’s [15] terminology and the (sets of) variables X, Y ,H1,H2, we have from expression (3.1) that π(y ‖ x) = ∑ h1,h2 [ π(x,h1,h2, y) π(x | pa(x)) ] Note that (X,H1) H2 ⇒ X H2 | H1, so we can equate PA(X) with H1, and write π(y ‖ x) = ∑ h1,h2 [ π(x | h1) ]−1 π(x,h1)π(h2, y | h1, x) = ∑ h”
Counterfactual analyses with graphical models based on local independence (2011, background): “3) is known as the ordered directed Markov Property and was shown to be equivalent to the local directed Markov property in [16], Theorem 2.”
Causal identifiability via Chain Event Graphs (2013, background): “Since then Dawid [5] and Lauritzen [11] have produced versions of the Back Door theorem which incorporate similar causal assumptions but express the conditions in terms of conditional independence statements.”