The use of intervention for time series modelling is a well established technique for on-line forecasting and decision-making in the context of Bayesian dynamic linear models. Intervention has also been recently used in (non-dynamic) Bayesian networks to investigate causal relationships between variables, and in dynamic Bayesian networks to investigate lagged causal relationships between time s...

People often struggle when making Bayesian probabilistic estimates on the basis of competing sources of statistical evidence. Recently, Krynski and Tenenbaum (2007) proposed that a causal Bayesian framework accounts for peoples’ errors in Bayesian reasoning, and showed that by clarifying the causal relations amongst the pieces of evidence, judgements on a classic statistical reasoning problem c...

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...

So causal models need to be able to treat causal relationships as causes and effects. This observation motivates an extension the Bayesian network causal calculus (Section 2) to allow nodes that themselves take Bayesian networks as values. Such networks will be called recursive Bayesian networks (Section 3). Because recursive Bayesian networks make causal and probabilistic claims at different l...

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...

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

In this paper we propose a causal analog to the purely observational Dynamic Bayesian Networks, which we call Dynamic Causal Networks. We provide a sound and complete algorithm for identification of Dynamic Causal Networks, namely, for computing the effect of an intervention or experiment, based on passive observations only, whenever possible. We note the existence of two types of confounder va...