Causal knowledge plays a crucial role in human thought, but the nature of causal
representation and inference remains a puzzle. Can human causal inference be captured by …

This chapter reviews empirical and theoretical results concerning knowledge of causal
mechanisms—beliefs about how and why events are causally linked. First, it reviews the …

Inducing causal relationships from observations is a classic problem in scientific inference,
statistics, and machine learning. It is also a central part of human learning, and a task that …

Causation is a central topic in many areas of philosophy. In metaphysics, philosophers want
to know what causation is, and how it is related to laws of nature, probability, action, and …

Imagine a person with no causal knowledge or concept of cause and effect. That person
would be like one of Plato's cave dwellers—destined to watch the shifting shadows of sense …

Can people learn causal structure more effectively through intervention rather than
observation? Four studies used a trial-based learning paradigm in which participants …

How do people learn causal structure? In 2 studies, the authors investigated the interplay
between temporal-order, intervention, and covariational cues. In Study 1, temporal order …

Time plays a pivotal role in causal inference. Nonetheless most contemporary theories of
causal induction do not address the implications of temporal contiguity and delay, with the …

Research on causal cognition has largely focused on learning and reasoning about
contingency data aggregated across discrete observations or experiments. However, this …

Conviction Narrative Theory (CNT) is a theory of choice under radical uncertainty–situations
where outcomes cannot be enumerated and probabilities cannot be assigned. Whereas …