Representing and Solving Decision Problems with Limited Information

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Influence Diagrams for Causal Modelling and Inference (2002, methodology): “We shall base our development on the more general ‘limited memory influence diagrams’ (Nilsson & Lauritzen, 2000; Lauritzen & Nilsson, 2001).”
Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings (2019, methodology): “g the following rules: a) Go backwards without passing any marked node. At any point, switch to step b. b) Go forward without passing any marked node. When reaching a marked node, switch to step a. 6 Lauritzen and Nilsson (2001) also show how the onlyif part of the criterion is extended to CIDs with”
Causal Inference from Graphical Models (2001, background): “I Causal Bayesian networks Structural equation systems Computation of effects References Assessment of effects of actions Intervention diagrams LIMIDs G-computation a l b r t - t - t @ @ @I 6 t p(1r || 1a, 1b) = ∑ xl p(1r , xl || 1a, 1b) = ∑ xl p(1r | xl , 1a, 1b)p(xl | 1a).”
Optimal dynamic treatment regimes (2003, unknown): “Some recent work by statisticians in this setting includes Shachter (1986), Owens et al. (1997), Cowell et al. (1999), chapter 8, and Lauritzen and Nilsson (2001) all of whom used operations on influence diagrams to ascertain optimal sequential decision rules.”
Agent Incentives: A Causal Perspective (2021, methodology): “Definition 7 (Nonrequisite observation; Lauritzen and Nilsson 2001).”