Causal learning : psychology, philosophy, and computation

By Alison Gopnik, Laura Schulz. 2007

437 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cb88110be710d68c518486f2ee2aeb15d2042b83

How researchers see this paper

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

In Artificial Intelligence

Citations in the corpus

Algorithms for Discovery of Multiple Markov Boundaries (2013, background): “Keywords: Markov boundary discovery, variable/feature selection, information equivalence, violations of faithfulness”
Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research (2011, background): “To further examine these, I draw on the causal attribution (e.g., Gopnik & Schulz, 2007; Kim, Dirks, Cooper, & Ferrin, 2006; Sloman, 2005; Waldman, Hagmayer, & Blaisdell, 2006) and the cognitive strategy literatures (e.g., Abrahamson & Fombrun, 1994; Barr et al., 1992; Huff, 1990; Reger & Huff,…”
Using Causal Induction in Humans to Learn and Infer Causality from Video (2013, background): “Although Bayesian networks are commonly used to represent causality (Pearl, 2009), grammar models have the expressive power to represent a greater breadth of possibilities than a single instance of a Bayesian network (Grifﬁths & Tenenbaum, 2007).”
Where science starts: spontaneous experiments in preschoolers' exploratory play. (2011, positive): “…everyday knowledge has the structural, functional, and dynamic properties of scientific theories, has been among the most influential accounts of cognitive development in recent decades (Carey, 1985, 2000; Gelman, 2003; Gopnik & Meltzoff, 1997; Gopnik & Schulz, 2007; Keil, 1989; Wellman, 1990).”
Causal-explanatory pluralism: How intentions, functions, and mechanisms influence causal ascriptions. (2010, background): “But by employing artifacts and biological traits Experiment 2 can more decisively sidestep the concern that evaluations of moral responsibility are responsible for the patterns of causal ratings in these experiments.”
Limits on simulation approaches in intuitive physics. (2021, neutral): “…reason about qualitative relationships between variables (Forbus, 1984; Forbus & Gentner, 1983; Klenk & Forbus, 2009), abstract causal reasoning (Griffiths & Tenenbaum, 2007; Holmes & Wolff, 2010; Sloman, 2005; Wolff, 2007; Wolff & Barbey, 2015), analogy (Gentner, 1983; Gentner & Gentner,…”
Belief net structure learning from uncertain interventions (2007, background): “In particular, there is much interest in learning the structure of these models from data, particularly in the area of systems biology (Friedman, 2004) and cognitive science (Gopnik and Schulz, 2007).”