Learning a theory of causality.

By Noah D. Goodman, Tomer D. Ullman, Josh Tenenbaum. 2011

225 citations recorded by the source corpus.

Subfields: Religious studies, Sociology and Political Science, Causal Inference & Discovery

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Citations in the corpus

Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, background): “This kind of knowledge is known as domain-general knowledge in cognitive science (Goodman et al. 2011).”
Causal Reasoning from Meta-reinforcement Learning (2019, background): “Analogous (Grant et al., 2018) models that learn causal structure directly from the environment, rather than having a pre-conceived formal theory, have also been implicated in human intelligence (Goodman et al., 2011).”
Finding new facts; thinking new thoughts. (2012, background): “ABSTRACT ERROR MAPS, QUINIAN BOOTSTRAPPING, AND ANALOGICAL REASONING The ideas behind theory grammars and stochastic search (Goodman et al., 2011; Ullman et al., 2012) were themselves partly motivated by another account of how learners might move beyond hypothesis selection: Quinian bootstrapping…”
Probabilistic Meta-Representations Of Neural Networks (2018, unknown): “on shift by re-aligning the networks to the new data. Our model handles a variety of tasks without requiring task-dependent manually-imposed structure, as it beneﬁts from the blessing of abstraction [Goodman et al., 2011] which arises when rich structured representations emerge from hierarchical mod”
From Dependence to Causation (2016, background): “Paraphrasing Goodman et al. (2011), is the human sense of causation innate or learned?”
Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory (2012, unknown): “In fact, recent work on HBM has shown that sometimes abstract generalizations can actually precede specific ones (e.g., Goodman et al., 2011).”
Dynamic inference and everyday conditional reasoning in the new paradigm (2013, background): “14 In Grice’s (1975) terms, Barwise and Perry (1983) were proposing that natural meaning,”
Concepts in a Probabilistic Language of Thought (2014, unknown): “Margolis and Laurence, MIT Press.”
Coordinate to cooperate or compete: Abstract goals and joint intentions in social interaction (2016, background): “learned from a sufficiently rich hypothesis space or grammar of abstract knowledge (Goodman et al., 2011; Tenenbaumet al., 2011).”
The debate over understanding in AI’s large language models (2022, background): “Researchers disagree on the extent to which concepts are domain-specific and innate versus more general-purpose and learned [14, 30, 31, 53, 75, 85], the degree to which concepts are grounded via embodied metaphors [28, 46, 61], are represented in the brain via dynamic, situation-based simulations [”
On the nature and origin of intuitive theories : learning, physics and psychology (2015, background): “The core of our formal treatment is a hierarchical probabilistic generative model for theories [92, 187, 62], specialized to the domain of intuitive physical theories (Fig.”