Article

Building Compressed Causal Models of the World

2023-06-20

Abstract excerpt

<p>A given causal system can be represented in a variety of ways. How do agents determine which variables to include in their causal representations, and at what level of granularity? Using techniques from Bayesian networks, information theory, and decision theory, we develop a formal theory according to which causal representations reflect a trade-off between compression and informativeness, where the optimal tra...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: c5690d03-d97d-5a21-9cd9-02320f2287dd
DOI: 10.31234/osf.io/2f7x6

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Building Compressed Causal Models of the WorldDOI 10.31234/osf.io/2f7x6Read

Breaking the chains of independence: A Bayesian uncertainty model of normative violations in human causal probabilistic reasoningSemantic proximity Read

Instrumental Causal LearningSemantic proximity Read

Rational causal induction from events in timeSemantic proximity Read

Productive causation and compositionalitySemantic proximity Read

Empowerment Gain and Causal Model Construction: Children and adults are sensitive to controllability and variability in their causal interventionsSemantic proximity Read

Do people use counterexamples or subjective probabilities in uncertain conditional reasoning? Investigating human reasoning with an open response formatSemantic proximity Read

Looking back to plan ahead: Causal judgments as a sampling approximation for action effectivenessSemantic proximity Read

Base rate neglect and conservatism in probabilistic reasoning: Insights from eliciting full distributionsSemantic proximity Read

Causal AI for Data Scientists: A Framework to Automate Discovery of Causal Relationships in Noisy DatasetsSemantic proximity Read