A theory of causal learning in children: causal maps and Bayes nets.

By Alison Gopnik, Clark Glymour, David M. Sobel, Laura Schulz, Tamar Kushnir, David Danks. 2004

1,155 citations recorded by the source corpus.

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, Probabilistic & Bayesian Methods

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Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, background): “The underlying cognitive representations can be understood as ‘intuitive theories’, with a causal structure resembling a scientific theory (Carey, 2004, 2009; Gopnik et al., 2004; Gopnik & Meltzoff, 1999; Gweon, Tenenbaum, & Schulz, 2010; L. Schulz, 2012; H. Wellman & Gelman, 1998; H. M. Wellman & G”
Toward Causal Representation Learning (2021, background): “Work in developmental psychology argues that there is a need to experiment in order to discover causal relationships [81].”
Towards Causal Representation Learning (2021, background): “Work in developmental psychology argues that there is a need to experiment in order to discover causal relationships [80].”
Causal Reasoning from Meta-reinforcement Learning (2019, background): “Causal reasoning may be an essential component of natural ∗ Corresponding author: ishitadasgupta@g.harvard.edu intelligence and is present in human babies, rats, and even birds (Leslie, 1982; Gopnik et al., 2001; 2004; Blaisdell et al., 2006; Lagnado et al., 2013).”
Judgments of cause and blame: the effects of intentionality and foreseeability. (2008, background): “He builds on the pioneering work of Heider (1958) and Kelley (1973) in social psychology, but also draws on work by legal theorists (Hart & Honoré, 1985) and philosophers (e.”
The Emergence of Scientific Reasoning (2012, background): “Young children are sometimes deemed “little scientists” because they appear to have abilities that are used in formal scientific reasoning (e.g., causal reasoning; Gopnik et al., 2004).”
Deep Nets: What have They Ever Done for Vision? (2018, background): “2016) and are not passive observers but instead interact with the world behaving like “baby scientists” who seek to understand the causal structure of the world (Gopnik et al., 2004).”
The Early Ontogeny of Social Norms (2013, background): “Regarding causal learning, recent work has shed considerable light on the developing mechanisms underlying the inductive learning of causal regularities from limited observations (Gopnik et al., 2004).”
Preschool children learn about causal structure from conditional interventions. (2007, background): “…Tenenbaum, Wagenmakers & Blum, 2003; Tenenbaum & Griffiths, 2003; Waldmann & Hagmayer, 2001) and children (Gopnik, Sobel, Schulz & Glymour, 2001; Gopnik, Glymour, Sobel, Schulz, Kushnir & Danks, 2004; Schulz & Gopnik, 2004, ; Sobel, Tenenbaum & Gopnik, 2004; Kushnir & Gopnik, 2005, in press).”
Intuitive Theories (2020, background): “Most of the empirical support for the Heiderian view of man (or child) as intuitive theorist comes from developmental psychology (e.g. Gopnik et al., 2004; Gweon & Schulz, 2011; Schulz, 2012; for reviews, see Flavell, 1999; Saxe, Carey, & Kanwisher, 2004).”
Enhanced Memory as a Common Effect of Active Learning (2016, background): “…examined how people make active interventions to generate evidence that distinguishes between possible causal explanations (Coenen, Rehder, & Gureckis, 2015; Gopnik et al., 2004; Lagnado & Sloman, 2004; Rottman & Keil, 2012; Sobel & Kushnir, 2006; Steyvers, Tenenbaum, Wagenmakers, & Blum, 2003).”