Weak causal relationships and small sample size pose two significant difficulties to the automatic discovery of causal models from observational data. This paper examines the influence of weak causal links and varying sample sizes on the discovery of causal models. The experimental results i l lustrate the effect of larger sample sizes for discovering causal models reliably and the relevance of...

Rational models of causal induction have been successful in accounting for people’s judgments about causal relationships. However, these models have focused on explaining inferences from discrete data of the kind that can be summarized in a 2×2 contingency table. This severely limits the scope of these models, since the world often provides non-binary data. We develop a new rational model of ca...

Reductionist science produces causal models of small fragments of complicated systems. Causal models of entire systems can be hard to construct because what is known of them is distributed across a vast amount of literature. The Big Mechanism program aims to have machines read the literature and assemble the causal fragments found in individual papers into huge causal models, automatically. The...

We introduce a new family of graphical models that consists of graphs with possibly directed, undirected and bidirected edges but without directed cycles. We show that these models are suitable for representing causal models with additive error terms. We provide a set of sufficient graphical criteria for the identification of arbitrary causal effects when the new models contain directed and und...

Automating the learning of causal models from sample data is a key step toward incorporating machine learning into decisionmaking and reasoning under uncertainty. This paper presents a Bayesian approach to the discovery of causal models, using a Minimum Message Length (MML) method. We have developed encoding and search methods for discovering linear causal models. The initial experimental resul...

One of the goals of the social sciences is to explain social phenomena. In this explanatory enterprise, causal relations, established by means of the so-called ‘causal models’, play a major role. This paper advances the view that causal models, by modelling causal mechanisms, (ought to) provide an explanation of social phenomena and should be seen as a model of explanation.1

Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this paper, we present the EM algorithm to learn the parameters in causal independence models based on the symmetric Boolean function. The developed algorithm enables us to assess the practical usefulness of the symmetric cau...

We present a framework for the rational analysis of elemental causal induction – learning about the existence of a relationship between a single cause and effect – based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship exists and asking how strong that causal relat...

One type of scientific inquiry involves the analysis of large data sets, often using statistical models and formal tests of hypotheses. A moment’s thought, however, shows that there must be other types of scientific inquiry. For instance, something has to be done to answer questions like the following. How should a study be designed? What sorts of data should be collected? What kind of a model ...

Numerous studies of how people reason with statistical data suggest that human judgment often fails to approximate rational probabilistic (Bayesian) inference. We argue that a major source of error in these experiments may be misunderstanding causal structure. Most laboratory studies demonstrating probabilistic reasoning deficits fail to explain the causal relationships behind the statistics pr...