Influence Diagrams for Causal Modelling and Inference

By A. P. Dawid. 2002

296 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Causal inference in statistics: An overview (2009, background): “The unification of the graphical, potential outcome, structural equations, decision analytical (Dawid, 2002), interventional (Woodward, 2003), sufficient component (Rothman, 1976) and probabilistic (Suppes, 1970) ap-”
Detection of Unfaithfulness and Robust Causal Inference (2008, background): “Recent work on causal modeling and reasoning (e.g., Pearl 2000, Spirtes et al. 2000, Dawid 2002) has emphasized an important kind of inductive problem: how to infer what would happen to a unit or a system if the unit or system were intervened upon to change in some way, based on observations of…”
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions (2019, methodology): “The same construct was used more prominently in [6] in the context of inference and identification.”
Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings (2019, background): “Fortunately, it is straightforward to make a similar move for influence diagrams, and add a causal interpretation to the edges without changing any of the definitions (Dawid, 2002).”
Causal Transfer Learning (2017, background): “(4)Our graphical representation bears strong similarities with influence diagrams [Dawid, 2002], but a formal difference is that we consider the intervention variables to be random variables that reflect the empirical distribution of the experimental design, whereas in influence diagrams they are in”
Transportability across studies: A formal approach (2011, background): “Fleming and DeMets (1996) leave the impression that correlation fails to serve surrogacy only when it is weak or moderate, while Baker and Kramer (2003) conclude that correlation fails because it is hard, in practice, to accurately estimate slopes and intercepts.”
Agent Incentives: A Causal Perspective (2021, methodology): “An informal precursor of the SCIM that also used structural functions (as opposed to conditional probability distributions) was the “functional influence diagram” (Dawid 2002).”
Joint Causal Inference from Multiple Contexts (2016, methodology): “, using influence diagrams (Dawid, 2002), using selection diagrams (Bareinboim and Pearl, 2013), considering only conditional models (i.”
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.”
Causal Bounds and Observable Constraints for Non-deterministic Models (2012, background): “, 1993; Pearl, 1993), with conditional independence assumptions (Lauritzen, 2001; Dawid, 2002).”
A new criterion for confounder selection (2011, background): “Further discussion of the causal interpretation of directed acyclic graphs can be found elsewhere (Spirtes, Glymour, and Scheines, 1993; Pearl, 1995, 2009; Dawid, 2002; Richardson and Spirtes, 2002; Robins, 2003; Shpitser and Pearl, 2008).”
Making Decisions that Reduce Discriminatory Impacts (2019, unknown): “As commonly done in the literature (Spirtes et al., 1993; Pearl, 2000; Dawid, 2002), we can represent interventions as special types of vertices in a causal graph.”