In this paper, we apply a recently developed differential approach to inference in staged tree models to causal inference. Staged trees generalise modelling techniques established for Bayesian networks (BN). They have the advantage that they can depict highly nuanced structure impossible to express in a BN and also enable us to perform causal manipulations associated with very general types of ...

Bayesian knowledge bases (BKBs) are a gen­ eralization of Bayes networks and weighted proof graphs (WAODAGs), that allow cycles in the causal graph. Reasoning in BKBs re­ quires finding the most probable inferences consistent with the evidence. The cost­ sharing heuristic for finding least-cost ex­ planations in WAODAGs was presented and shown to be effective by Charniak and Hu­ sain. However, ...

Discrimination discovery is to unveil discrimination against a specific individual by analyzing the historical dataset. In this paper, we develop a general technique to capture discrimination based on the legally grounded situation testing methodology. For any individual, we find pairs of tuples from the dataset with similar characteristics apart from belonging or not to the protected-by-law gr...

We present a symbolic machinery that admits both probabilistic and causal information about a given domain and produces probabilistic statements about he effect of actions and the impact of observations. Thecalculus admits two types of conditioning operators: ordinary Bayes conditioning, P(y]X = z), which represents he observation X z, and causal conditioning, P(yldo(X = x)), read the probabili...

Reasoning by jurors concerning whether an accused person should be convicted of committing a crime is a kind of casual inference. Jurors need to decide whether the evidence in the case was caused by the accused’s criminal action or by some other cause. This paper compares two computational models of casual inference: explanatory coherence and Bayesian networks. Both models can be applied to leg...

This study proposes a model using a Bayesian network to understand tactical behavior in Karate matches. The model is a probabilistic causal model consisting of the states of two competitors engaged in combat. Each state node of the model outputs a probability distribution of the occurrence of offensive, defensive, and evaluative actions. Using the model, we also propose an analysis method of Ka...

In recent years, a number of algorithms have been developed for learning the structure of Bayesian networks from data. In this paper we apply some of these algorithms to a realistic medical domain—stroke. Basically, the domain of stroke is taken as a typical example of a medical domain where much data are available concerning a few hundred patients. Learning the structure of a Bayesian network ...

There has been little research on infants’ development of causal inference in the second year after birth. We report an experiment in which 9to 18-month-old infants viewed visual sequences consisting of three looming shapes, one after another. Half of the sequences (causes) were predictive of an attention-getting reward (effect), and the other half were nonpredictive. The statistical complexity...

Bayesian networks can be used to extract explanations about the observed state of a subset of variables. In this paper, we explicate the desiderata of an explanation and confront them with the concept of explanation proposed by existing methods. The necessity of taking into account causal approaches when a causal graph is available is discussed. We then introduce causal explanation trees, based...

Causal modeling, such as noisy-OR, reduces probability parameters to be acquired in constructing a Bayesian network. Multiple causes can reinforce each other in producing the effect or can undermine the impact of each other. Most existing causal models do not consider their interactions from the perspective of reinforcement or undermining. We show that none of them can represent both interactio...