Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations of the co-occurrence frequencies between causes and effects, interactions between physical objects, or patterns of spatial or temporal coincidence. These different modes of learning are typically thought of as distinct psychological processes and are rarely studied together, but at heart they present the same inductive challenge—identifying the unobservable mechanisms that generate observable relations between variables, objects, or events, given only sparse and limited data. We present a computational-level analysis of this inductive problem and a framework for its solution, which allows us to model all these forms of causal learning in a common language. In this framework, causal induction is the product of domain-general statistical inference guided by domain-specific prior knowledge, in the form of an abstract causal theory. We identify 3 key aspects of abstract prior knowledge—the ontology of entities, properties, and relations that organizes a domain; the plausibility of specific causal relationships; and the functional form of those relationships—and show how they provide the constraints that people need to induce useful causal models from sparse data.

The dynamics model, which is based on L. Talmy’s (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and explains the induction of causal relationships from single observations. Support for the model is provided in experiments in which participants categorized 3-D animations of realistically rendered objects with trajectories that were wholly determined by the force vectors entered into a physics simulator. Experiments 1–3 showed that causal judgments are based on several forces, not just one. Experiment 4 demonstrated that people compute the resultant of forces using a qualitative decision rule. Experiments 5 and 6 showed that a dynamics approach extends to the representation of social causation. Implications for the relationship between causation and time are discussed.

Our main goal in the present set of studies was to re-visit the question whether people are capable of inducing causal models from covariation data alone without further cues, such as temporal order. In the literature there has been a debate between bottom-up and top-down learning theories in causal learning. Whereas top-down theorists claim that in structure induction, covariation information plays none or only a secondary role, bottom-up theories, such as causal Bayes net theory, assert that people are capable of inducing structure from conditional dependence and independence information alone. Our three experiments suggest that both positions are wrong. In simple three-variable domains people are indeed often capable of reliably picking the right model. However, this can be achieved by simple heuristics that do not require complex statistics.

A Bayesian network (BN) is a graphical model of uncertainty that is especially wellsuited to legal arguments. It enables us to visualise and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments there is no systematic, repeatable method for modelling legal arguments as BNs. Hence, where BNs have been used in the legal context, they are presented as completed pieces of work, with no insights into the reasoning and working that must have gone into their construction. This means the process of building BNs for legal arguments is ad-hoc, with little possibility for learning and process improvement. This paper directly addresses this problem by describing a method for building useful legal arguments in a consistent and repeatable way. The method complements and extends recent work by Hepler, Dawid and Leucari on objected-oriented BNs for complex legal arguments and is based on the recognition that such arguments can be built up

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Agents situated in a dynamic environment with an initially unknown causal structure, which, moreover, links certain behavioral choices to rewards, must be able to learn such structure incrementally on the fly. We report an experimental study that characterizes human learning in a controlled dynamic game environment, and describe a computational model that is capable of similar learning. The model learns by building up a representation of the hypothesized causes and effects, including estimates of the strength of each causal interaction. It is driven initially by simple guesses regarding such interactions, inspired by events occurring in close temporal succession. The model maintains its structure dynamically (including omitting or even reversing the current best-guess dependencies, if warranted by new evidence), and estimates the projected probability of possible outcomes by performing inference on the resulting Bayesian network. The model reproduces the human performance in the present dynamical task.