Theory-based causal induction.

By T. Griffiths, Josh Tenenbaum. 2009

388 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Generalization in anti-causal learning (2018, background): “A theory in cognitive psychology suggests that humans induce causal relations from observation by domain-general statistical inference (search) that is guided by domain-specific prior knowledge (heuristics) (Griffiths and Tenenbaum, 2009).”
Causal imprinting in causal structure learning. (2012, neutral): “When learning only a single causal relation, people tend to give approximately normative judgments , consistent with statistical models of causal inference (Buehner et al., 2003; Griffiths & Tenenbaum, 2005, 2009; Rottman, Ahn, & Luhmann, 2011).”
Children's imitation of causal action sequences is influenced by statistical and pedagogical evidence. (2011, methodology): “As in previous work on children's causal inference, we use a deterministic-OR model (Griffiths & Tenenbaum, 2009), in which any of the correct sequences will always bring about the effect.”
Formalizing Neurath’s Ship: Approximate Algorithms for Online Causal Learning (2016, background): “…make broadly normative causal inferences based on cues like observed contingencies or the outcomes of interventions, which are manipulations or tests of the system (e.g. Griffiths & Tenenbaum, 2009; Holyoak & Cheng, 2011; Lagnado & Sloman, 2004, 2006; Lagnado, Waldmann, Hagmayer, & Sloman, 2007).”
Intuitive theories as grammars for causal inference (2007, neutral): “In a series of papers (Griffiths, Baraff, & Tenenbaum, 2004; Griffiths & Tenenbaum, in press, in preparation; Tenenbaum & Griffiths, 2001; 2003; Steyvers, Tenenbaum, Wagenmakers, & Blum, 2003) we have shown how this theory-based Bayesian framework can be used to build rational and quantitatively…”
Consequences of the Now-or-Never bottleneck for signed versus spoken languages (2015, background): “…with a widespread interest in rational, rather processing-based, explanations in cognitive science (e.g., Anderson 1990; Chater et al. 2006 Griffiths & Tenenbaum 2009; Oaksford & Chater 1998; 2007; Tenenbaum et al. 2011), including language processing (Gibson et al. 2013; Hale 2001; 2006;…”
Learning a theory of causality. (2011, background): “…Waldmann & Martignon, 1998; Steyvers, Tenenbaum, Wagenmakers, & Blum, 2003; Gopnik et al., 2004; Lu, Yuille, Liljeholm, Cheng, & Holyoak, 2008; Griffiths & Tenenbaum, 2005, 2009) has described mathematical models of how children and adults learn domain-specific causal relations by applying…”
Bayesian models of cognition (2008, background): “…cases, including learning in larger causal networks (Steyvers et al., 2003), learning about dynamic causal relationships in physical systems (Tenenbaum & Griﬃths, 2003), choosing which interventions to perform in the aid of causal learning (Steyvers et al., 2003), learning about hidden…”
Intuitive Theories (2020, background): “…have been overcome by developing richer frameworks such as hierarchical CBNs that capture causal dependencies on multiple levels of abstraction (Griffiths & Tenenbaum, 2009; Tenenbaum, Griffiths, & Kemp, 2006; Tenenbaum, Kemp, Griffiths, & Goodman, 2011), or frameworks that combine CBNs with…”
Inferring action structure and causal relationships in continuous sequences of human action. (2015, neutral): “It is worth emphasizing that, unlike previous Bayesian models of causal inference (Griffiths & Tenenbaum, 2005, 2009), we do not start with a fixed, known set of potential causes.”
Concepts in a Probabilistic Language of Thought (2014, neutral): “…given real insight into many cognitive domains: inductive reasoning (Kemp & Tenenbaum, 2009), causal learning (Goodman, Ullman, & Tenenbaum, 2011; Griffiths & Tenenbaum, 2009), word learning (M. C. Frank, Goodman, & Tenenbaum, 2009; Piantadosi, Tenenbaum, & Goodman, 2012), and mental state…”
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, neutral): “In this paper we review relevant background theory and provide a survey of methods which perform structure discovery (sometimes called causal induction [80]) with observational data or with a mixture of observational and experimental data.”
Reconciling intuitive physics and Newtonian mechanics for colliding objects. (2013, methodology): “The noisy Newton framework model follows the probabilistic approach of Griffiths and Tenenbaum (2009), which as noted by Rips (2011) is a version of a schema model.”