In this paper we introduce chain multi-agent causal models which are an extension of causal Bayesian networks to a multi-agent setting. Instead of 1 single agent modeling the entire domain, there are several agents organised in a chain, each modeling non-disjoint subsets of the domain. Every agent has a causal model over the variables in his domain, determined by an acyclic causal diagram and a...

We present two Bayesian algorithms CD-B and CD-H for discovering unconfounded cause and effect relationships from observational data without assuming causal sufficiency which precludes hidden common causes for the observed variables. The CD-B algorithm first estimates the Markov blanket of a node X using a Bayesian greedy search method and then applies Bayesian scoring methods to discriminate t...

Evidence can be complex in various ways: e.g., it may exhibit structural complexity, containing information about causal, hierarchical or logical structure as well as empirical data, or it may exhibit combinatorial complexity, containing a complex combination of kinds of information. This paper examines evidential complexity from the point of view of Bayesian epistemology, asking: how should co...

Discovering relationships of the form “A causally influences B” is valuable in different fields of study. These relationships are also referred to as “cause and effect” relationships where A represents the cause, and B denotes the effect. Generally, experimental studies are performed to ascertain causality where the value of a variable is set randomly and its effects measured under controlled e...

Discovering causal structure from observational data in the presence of latent variables remains an active research area. Constraint-based causal discovery algorithms are relatively efficient at discovering such causal models from data using independence tests. Typically, however, they derive and output only one such model. In contrast, Bayesian methods can generate and probabilistically score ...

We clarify the status of the so-called causal minimality condition in the theory of causal Bayesian networks, which has received much attention in the recent literature on the epistemology of causation. In doing so, we argue that the condition is well motivated in the interventionist (or manipulability) account of causation, assuming the causal Markov condition which is essential to the semanti...

Using single-nucleotide polymorphism (SNP) genotypes from the 1000 Genomes Project pilot3 data provided for Genetic Analysis Workshop 17 (GAW17), we applied Bayesian network structure learning (BNSL) to identify potential causal SNPs associated with the Affected phenotype. We focus on the setting in which target genes that harbor causal variants have already been chosen for resequencing; the go...

A causal graph G(V, E) specifies causal relationships among the random variables representing the vertices of the graph V . The relationships are specified by the directed edges E ; an edge Vi ! Vj implies that Vi 2 V is a direct parental cause for the effect Vj 2 V . With some abuse of notation, we will denote the random variable associated with a node V 2 V by V itself. We will denote the par...

Evidential reasoning is hard, and errors can lead to miscarriages of justice with serious consequences. Analytic methods for the correct handling of evidence come in different styles, typically focusing on one of three tools: arguments, scenarios or probabilities. Recent research used Bayesian Networks for connecting arguments, scenarios, and probabilities. Well-known issues with Bayesian Netwo...

The paper presents a structured modeling lan­ guage (SML) and a relational database framework for specification and automated genera­ tion of causal models. The framework describes a relational database scheme for encoding a li­ brary of causal network templates modeling the basic components in a modeling domain. SML provides a formal language for specifying mod­ els as structured components th...