Computation, causation, and discovery

By Mtw, Clark Glymour, Gregory F. Copper. 2000

356 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

The max-min hill-climbing Bayesian network structure learning algorithm (2006, background): “We call 〈G, P〉 a (discrete) Bayesian network if 〈G, P〉 satisfies the Markov Condition: every variable is independent of any subset of its non-descendant variables conditioned on its parents (Pearl, 1988; Spirtes, Glymour & Scheines, 1993, 2000; Glymour and Cooper, 1999; Pearl, 2000; Neapolitan, 2003”
Inferring subnetworks from perturbed expression profiles (2001, background): “First, we adapt and extend recent results on learning with interventions (Cooper and Yoo, 1999) to handle genetic mutations.”
Causality : Objectives and Assessment (2010, background): “Two approaches attempt to fill this vacuum: (1) Operational tests of causality (Glymour and Cooper, 1999) allow us to detect causality experimentally using controlled experiments, but they provide only sufficient criteria for causality, not necessary conditions (see Section 3.1).”
Structure and strength in causal induction. (2005, unknown): “Nodes in the graph represent variables, and directed edges represent causal connections between those variables (Glymour, 1998; Glymour & Cooper, 1999; Pearl, 2000; Spirtes et al. 1993).”