Causal reasoning is an important part of scientific thinking, and even young children can use causes to explain what they observe and to make predictions. Weight is an interesting type of cause because it is a nonobvious property, and thus is not readily observable. The first research question of my dissertation examines when children use this property as a cause. In Study 1, 2- to 5-year-old children completed three different tasks in which they had to use weight to produce effects; an object displacement task, a balance-scale task, and a tower building task. The children’s use of weight improved with age, with 4- and 5-year-olds showing above-chance performance on all tasks. The younger children’s performance was more variable across tasks, suggesting that the complexity of the problem may influence their use of weight. The second research question is whether children’s use of weight as a cause can be improved. To examine this question, I varied the pedagogical cues that children received on the balance scale task from Study 1. The results of Study 2, indicate that highlighting the different effects of the heavy and light objects improves 3- to 4-year-olds’ performance. However, the results of Study 3 indicate that 2-year-olds did not benefit from even multiple pedagogical cues (contrasting the different effects and providing a verbal description to highlight the weight difference). To sum up, children at age 4 and above showed a general ability to use weight in across causal reasoning tasks. Whether children’s understanding of weight could be improved depended on their age and the cues given.

Causal reconstruction is the task of reading a written causal description of a physical behavior, forming an internal model of the described activity, and demonstrating comprehension through question answering. T his task is difficult because written d escriptions often do not specify exactly how r eferenced events fit together. This article (1) ch aracterizes the causal reconstruction problem, (2) presents a representation called transition space, which portrays events in terms of "transitions,'' or collections of changes expressible in everyday language, and (3) describes a program called PATHFINDER, which uses the transition space representation to perform causal reconstruction on simplified English descriptions of physical activity.

This report describes a paradigm for combining associational and causal reasoning to achieve efficient and robust problem-solving behavior. The Generate, Test and Debug (GTD) paradigm generates initial hypotheses using associational (heuristic) rules. The tester verifies hypotheses, supplying the debugger with causal explanations for bugs found if the test fails. The debugger uses domain-independent causal reasoning techniques to repair hypotheses, analyzing domain models and the causal explanations produced by the tester to determine how to replace faulty assumptions made by the generator. We analyze the strengths and weaknesses of associational and causal reasoning techniques, and present a theory of debugging plans and interpretations. The GTD paradigm has been implemented and tested in the domains of geologic interpretation, the blocks world, and Tower of Hanoi problems.

Comparative analysis is the problem of predicting how a system will react to perturbations in its parameters, and why. For example, comparative analysis could be asked to explain why the period of an oscillating spring/block system would increase if the mass of the block were larger. This thesis formalizes the task of comparative analysis and presents two solution techniques: differential qualitative (DQ) analysis and exaggeration. Both techniques solve many comparative analysis problems, providing explanations suitable for use by design systems, automated diagnosis, intelligent tutoring systems, and explanation based generalization. This thesis explains the theoretical basis for each technique, describes how they are implemented, and discusses the difference between the two. DQ analysis is sound; it never generates an incorrect answer to a comparative analysis question. Although exaggeration does occasionally produce misleading answers, it solves a larger class of problems than DQ analysis and frequently results in simpler explanations.

The belief bias effect – the finding that prior beliefs influence judgments of logic and evidence – has been a topic of much empirical investigation in both deductive and causal reasoning. However, to date, no research has examined the degree to which such biases are the result of common or distinct mechanisms in these two domains. By using common scales of measurement, I examine the degree to which individuals show common biases in these two domains in two experiments. Surprisingly, although the belief bias effect was observed in both paradigms, biases in one domain were unreliably associated with biases in the other domain. Experiment 2 included 6 measures of individual differences in an attempt to uncover the observation of differential biases in these domains. Dogmatism was found to be the single most predictive measure of belief bias, but only in deductive reasoning. These data are discussed in terms of dual process theories of reasoning.

The belief bias effect – the finding that prior beliefs influence judgments of logic and evidence – has been a topic of much empirical investigation in both deductive and causal reasoning. However, to date, no research has examined the degree to which such biases are the result of common or distinct mechanisms in these two domains. By using common scales of measurement, I examine the degree to which individuals show common biases in these two domains in two experiments. Surprisingly, although the belief bias effect was observed in both paradigms, biases in one domain were unreliably associated with biases in the other domain. Experiment 2 included 6 measures of individual differences in an attempt to uncover the observation of differential biases in these domains. Dogmatism was found to be the single most predictive measure of belief bias, but only in deductive reasoning. These data are discussed in terms of dual process theories of reasoning.

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Yes / Understanding the cause of cost overruns in transportation infrastructure projects has been a topic that has received considerable attention from academics and the popular press. Despite studies providing the essential building blocks and frameworks for cost overrun mitigation and containment, the problem still remains a pervasive issue for Governments worldwide. The interdependency that exists between ‘causes’ that lead to cost overruns materialising have largely been ignored when considering the likelihood and impact of their occurrence. The vast majority of the cost overrun literature has tended to adopt a deterministic approach in examining the occurrence of the phenomenon; in this paper a shift towards the adoption of pluralistic probabilistic approach to cost overrun causation is proposed. The establishment of probabilistic theory incorporates the ability to consider the interdependencies of causes so to provide Governments with a holistic understanding of the uncertainties and risks that may derail the delivery and increase the cost of transportation infrastructure projects. This will further assist in the design of effective mitigation and containment strategies that will ensure future transportation infrastructure projects meet their expected costs as well as the need of taxpayers. / Australian Research Council (DP160102882)

In this dissertation, I propose a reductive account of causation. This account may be stated as follows: Causation: c is a cause of e within a possibility horizon ℓ if a) c is process-connected to e, and b) e security-depends on c within ℓ. More precisely, my suggestion is that there are two kinds of causal relata: instantaneous events (defined in Chapter 4) and possibility horizons (defined in Chapter 5). Causation is a ternary relation between two actual instantaneous events - the cause c and the effect e - and a possibility horizon ℓ. I argue that causation has a dual nature: on the one hand, a cause must be connected to its effect via a genuine process; on the other hand, a cause must make a difference to its effect. The first condition - namely, the condition of process-connection (defined in Chapter 6) - captures the sense in which a cause must be connected to its effect via a genuine process. This condition allows my account to separate causation from mere correlation, distinguish genuine causes from preempted backups, and capture how a cause must be at the right level of detail relative to its effect (Chapter 7). The second condition - namely, the condition of security-dependence (defined in Chapter 8) - captures the sense in which a cause must make a difference to its effect. This condition allows my account to yield intuitively correct verdicts on the counterexamples to the transitivity and intrinsicness of causation, resolve the problem of profligate omissions, accommodate structurally isomorphic but causally different cases, and handle contrastive causal claims (Chapter 9 and 10). Finally, my proposed account of causation logically entails restricted versions of three important principles of causal reasoning concerning the sufficiency of counterfactual dependence for causation, and the transitivity and intrinsicness of causation (Chapter 11).