l present a cognitive model of the human ability to acquire causal relationships. I report on experimental evidence demonstrating that human learners acquire accurate causal relationships more rapidly when training examples ore consistent with a general theory of causality. This article describes o learning process thot uses a general theory of causality as background knowledge. The learning pr...

This paper first enlists the generic problems of alloy wheel machining and subsequently details on the process improvement of the identified critical-to-quality machining characteristic of A356 aluminum alloy wheel machining process. The causal factors are traced using the Ishikawa diagram and prioritization of corrective actions is done through process failure modes and effects analysis. Proce...

Causal inference tries to solve the following problem: given i.i.d. data from a joint distribution, one tries to infer the underlying causal DAG (directed acyclic graph), in which each node represents one of the observed variables. For approaching this problem, we have to make assumptions that connect the causal graph with the joint distribution. Independence-based methods like the PC algorithm...

In this paper, we present a process for linking organizational training efforts with defects causal analysis in software development organizations. The process is being implemented in a CMMI maturity level 3 organization. Since causal analysis is not an expected process area at maturity level three, key success factors for the implementation of the process are identified and analyzed. The concl...

This paper investigates a solution to the problem of causal ordering in message passing distributed systems Causal ordering is the restriction that messages are delivered in a fo fashion with respect to the global causal order between events in the system This is stronger than the condition that each local channel is fo In our algorithm causal ordering is implemented by having each process main...

This paper describes a Bayesian method for combining an arbitrary mixture of observational and experimental data in order to learn causal Bayesian networks. Observational data are passively observed. Experimental data, such as that produced by randomized controlled trials, result from the experimenter manipulating one or more variables (typically randomly) and observing the states of other vari...

This paper describes a Bayesian method for combining an arbitrary mixture of observational and experimental data in order to learn causal Bayesian networks. Observational data are passively observed. Experimental data, such as that produced by randomized controlled trials, result from the experimenter manipulating one or more variables (typically randomly) and observing the states of other vari...

Human beings show a robust nonindependence effect in causal reasoning: they predict that collateral effects should be correlated even given a common cause. This presents a problem for existing models of causal reasoning, as most predict independence. To deal with this problem, we propose an edge replacement process that builds up apparently probabilistic causal relations using hidden determinis...

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...

Bayesian networks (BNs) are rapidly becoming a leading tool in applied Artificial Intelligence (AI). BNs may be built by eliciting expert knowledge or learned via causal discovery programs. A hybrid approach is to incorporate prior information elicited from experts into the causal discovery process. We present several ways of using expert information as prior probabilities in the CaMML causal d...