We tackle the problem of how to use information from multiple (in)dependence models, representing results from different experiments, including background knowledge, in causal discovery. We introduce the framework of a causal system in an external context to derive a connection between strict conditional independencies and causal relations between variables. Constraint-based causal discovery is...

Causal learning is a fundamental ability that enables human reasoners to learn about the complex interactions in the world around them. The available evidence with children and adults, however, suggests that the mechanism or set of mechanisms that underpins causal perception and causal reasoning are not well understood; that is, it is unclear whether causal perception and causal reasoning are u...

Discovering causal relationships is a hard task, often hindered by the need for intervention, and often requiring large amounts of data to resolve statistical uncertainty. However, humans quickly arrive at useful causal relationships. One possible reason is that humans extrapolate from past experience to new, unseen situations: that is, they encode beliefs over causal invariances, allowing for ...

Causal inference in sensory cue combination is the process of determining whether multiple sensory cues have the same cause or different causes. Psychophysical evidence indicates that humans closely follow the predictions of a Bayesian causal inference model. Here, we explore how Bayesian causal inference could be implemented using probabilistic population coding and plausible neural operations...

We present a Bayesian model of causal learning that incorporates generic priors on distributions of weights representing potential powers to either produce or prevent an effect. These generic priors favor necessary and sufficient causes. The NS power model couples these priors with a causal generating function derived from the power PC theory (Cheng, 1997). We test this and other alternative Ba...

one of the most important objectives of any economic system is to achieve to low and stable inflation and sustained economic growth. in this study, first, bayesian causal is indentified effective factors on inflation and then using bayesian causal network and determining prior probabilities and posterior probabilities in different scenarios,it is discussed the impacts of this factors on inflati...

To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the "causal inference problem." Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining ps...

The article presents a Bayesian model of causal learning that incorporates generic priors--systematic assumptions about abstract properties of a system of cause-effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes--causes that are few in number and high in their individual powers to produce or prevent effects. The SS power model couples these gen...

In this article we demonstrate the usefulness of causal Bayesian networks as probabilistic reasoning systems. The biggest advantage of causal Bayesian networks over traditional probabilistic Bayesian networks is that they sometimes allow to perform causal inference, i.e. the calculation of the causal effect of one variable on other variables. We treat a state-of-the-art algorithm for performing...