Leading accounts of judgment under uncertainty evaluate performance within purely statistical frameworks, holding people to the standards of classical Bayesian (Tversky & Kahneman, 1974) or frequentist (Gigerenzer & Hoffrage, 1995) norms. We argue that these frameworks have limited ability to explain the success and flexibility of people's real-world judgments, and propose an alternative normat...

Causal discovery is highly desirable in science and technology. In this paper, we study a new research problem of discovery of causal relationships in the context of streaming features, where the features steam in one by one. With a Bayesian network to represent causal relationships, we propose a novel algorithm called causal discovery from streaming features (CDFSF) which consists of a two-pha...

People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayes...

Most current theories of human causal learning are essentially parameter estimators: they assume a fixed causal structure and estimate causal strengths within that structure. In these theories, absence of causation is represented as zero causal strength, rather than a distinct causal structure. In this paper, we first present the theoretical framework of Bayesian networks, which can represent b...

Three studies reexamined the claim that clarifying the causal origin of key statistics can increase normative performance on Bayesian problems involving judgment under uncertainty. Experiments 1 and 2 found that causal explanation did not increase the rate of normative solutions. However, certain types of causal explanation did lead to a reduction in the magnitude of errors in probability estim...

tive coding framework does not yet make stringent commitments as to the nature of the causal models that the brain can represent. Hence, contrary to suggestions by Clark (in press), the framework does not yet have the virtue that it effectively implements tractable Bayesian inference. At this point in time three mutually exclusive options remain open: either predictive coding does not implement...

Causal networks (CNs) have been used to construct inference systems for diagnostics and decision making. More recently, Bayesian causal networks (BCNs) and fuzzy causal networks (FCNs) have gained considerable attention and offer an alternative framework for representing structured human knowledge and are used in causal inference in many real-world applications. However, for large systems, it i...

The principle of Kolmogorov Minimal Sufficient Statistic (KMSS) states that a model should capture all regularities of the data. The conditional independencies following from the causal structure of the system are the regularities incorporated in a graphical causal model. We prove that for joint probability distributions, the KMSS is described by the Directed Acyclic Graph (DAG) of the minimal ...

We examine the Bayesian approach to the discovery of directed acyclic causal models and compare it to the constraint-based approach. Both approaches rely on the Causal Markov assumption, but the two di er signi cantly in theory and practice. An important di erence between the approaches is that the constraint-based approach uses categorical information about conditional-independence constraints...