Correspondent inferences in attribution theory deal with assigning causes to behaviour based on true dispositions rather than situational factors. In this paper, we investigate how knowledge representation tools in Artificial Intelligence (AI), such as Bayesian networks (BNs), can help represent such situations and distinguish between the types of clues used in assessing the behaviour (disposit...

The relationship between algebraic geometry and the inferential framework of the Bayesian Networks with hidden variables has now been fruitfully explored and exploited by a number of authors. More recently the algebraic formulation of Causal Bayesian Networks has also been investigated in this context. After reviewing these newer relationships, we proceed to demonstrate that many of the ideas e...

When inferring causal relationships, people are often faced with ambiguous evidence. Models of causal inference have taken different approaches to explain reasoning about such evidence. One approach – epitomized by Bayesian models of causal inference – defers judgment by representing uncertainty across multiple explanations. Another approach – usually adopted by associative models – approximate...

While there has been considerable research in learning Bayesian network structure from data, until recently most of this research assumed that every variable of interest may be jointly measured in a single dataset. In practice, however, it is often the case that researchers only have access to data that is distributed across multiple datasets, which share some variables, but have other unique v...

This paper addresses causal inference and modeling over event streams where data have high throughput, are unbounded, and may arrive out of order. The availability of large amount of data with these characteristics presents several new challenges related to causal modeling, such as the need for fast causal inference operations while ensuring consistent and valid results. There is no existing wo...

Bayesian networks are currently one of the most interesting techniques of artificial intelligence. They combine the readability of a knowledge representation by an intuitive causal graph and the effectiveness of a data representation that takes into account the uncertainty in reasoning. They are used in various applications. This paper presents some ideas on the concept of Bayesian networks. We...

The Millennium Villages Project (MVP) is a ten-year integrated rural development project implemented in ten sub-Saharan African sites. We describe the design for causal inference about the MVP’s effect on a variety of development indicators. Causal inference for the MVP context presents many challenges: a nonrandomized design, limited baseline data for candidate controls, and the assignment of ...

This paper claims that causal model theory describes the meaningful information of probability distributions after a factorization. If the minimal factorization of a distribution is incompressible, its Kolmogorov minimal sufficient statistics, the parents lists, can be represented by a directed acyclic graph (DAG). We showed that a faithful Bayesian network is a minimal factorization and that a...

This paper addresses causal inference and modeling over event streams where data have high throughput and are unbounded. The availability of large amount of data along with the high data throughput present several new challenges related to causal modeling, such as the need for fast causal inference operations while ensuring consistent and valid results. There is no existing work specifically fo...

This paper presents a causal simulation method for incompletely known dynamic systems in process engineering . The causal model of a process is represented as both a causal network of interacting elementary dynamic systems, called qualitative automata, influencing one another, and a set of qualitative constraints linking possibly several of such automata . Associated with each influence is a we...