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24
Time as guide to cause
"... How do people learn causal structure? In two studies we investigated the interplay between temporal order, intervention and covariational cues. In Study 1 temporal order overrode covariation information, leading to spurious causal inferences when the temporal cues were misleading. In Study 2 both te ..."
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Cited by 18 (2 self)
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How do people learn causal structure? In two studies we investigated the interplay between temporal order, intervention and covariational cues. In Study 1 temporal order overrode covariation information, leading to spurious causal inferences when the temporal cues were misleading. In Study 2 both temporal order and intervention contributed to accurate causal inference, well beyond that achievable through covariational data alone. Together the studies show that people use both temporal order and interventional cues to infer causal structure, and that these cues dominate the available statistical information. We endorse a hypothesisdriven account of learning, whereby people use cues such as temporal order to generate initial models, and then test these models against the incoming covariational data.
Interpreting Causality
 in the Health Sciences,‖ International Studies in the Philosophy of Science
, 2007
"... Perhaps the key philosophical questions concerning causality are the following: • what are causal relationships? • how can one discover causal relationships? ..."
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Cited by 14 (5 self)
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Perhaps the key philosophical questions concerning causality are the following: • what are causal relationships? • how can one discover causal relationships?
Foundations for Bayesian networks
, 2001
"... Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions, or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probabi ..."
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Cited by 11 (7 self)
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Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions, or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probability given to the probabilities specified in the network. In this chapter I argue that current foundations are problematic, and put forward new foundations which involve aspects of both the interpreted and the formal approaches. One standard approach is to interpret a Bayesian network objectively: the graph in a Bayesian network represents causality in the world and the specified probabilities are objective, empirical probabilities. Such an interpretation founders when the Bayesian network independence assumption (often called the causal Markov condition) fails to hold. In §2 I catalogue the occasions when the independence assumption fails, and show that such failures are pervasive. Next, in §3, I show that even where the independence assumption does hold objectively, an agent’s causal knowledge is unlikely to satisfy the assumption with respect to her subjective probabilities, and that slight differences between an agent’s subjective Bayesian network and an objective Bayesian network can lead to large differences between probability distributions determined by these networks. To overcome these difficulties I put forward logical Bayesian foundations in §5. I show that if the graph and probability specification in a Bayesian network are thought of as an agent’s background knowledge, then the agent is most rational if she adopts the probability distribution determined by the
The path analysis controversy: A new statistical approach to strong apWALLER AND MEEHL336 praisal of verisimilitude
 Psychological Methods
, 2002
"... A new approach for using path analysis to appraise the verisimilitude of theories is described. Rather than trying to test a model’s truth (correctness), this method corroborates a class of path diagrams by determining how well they predict intradata relations in comparison with other diagrams. The ..."
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Cited by 9 (1 self)
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A new approach for using path analysis to appraise the verisimilitude of theories is described. Rather than trying to test a model’s truth (correctness), this method corroborates a class of path diagrams by determining how well they predict intradata relations in comparison with other diagrams. The observed correlation matrix is partitioned into disjoint sets. One set is used to estimate the model parameters, and a nonoverlapping set is used to assess the model’s verisimilitude. Computer code was written to generate competing models and to test the conjectured model’s superiority (relative to the generated set) using diagram combinatorics and is available on the Web
Scientific Coherence and the Fusion of Experimental Results
"... A pervasive feature of the sciences, particularly the applied sciences, is an experimental focus on a few (often only one) possible causal connections. At the same time, scientists often advance and apply relatively broad models that incorporate many different causal mechanisms. We are naturally led ..."
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Cited by 7 (1 self)
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A pervasive feature of the sciences, particularly the applied sciences, is an experimental focus on a few (often only one) possible causal connections. At the same time, scientists often advance and apply relatively broad models that incorporate many different causal mechanisms. We are naturally led to ask whether there are normative rules for integrating multiple local experimental conclusions into models covering many additional variables. In this paper, we provide a positive answer to this question by developing several inference rules that use local causal models to place constraints on the integrated model, given quite general assumptions. We also demonstrate the practical value of these rules by applying them to a case study from ecology. 1 Experimental scope in applied sciences 2 Fusing the results of experiments 3 A concrete example of the inference rules 4 Application to a case study 1 Experimental scope in applied sciences Total photosynthetic material has increased globally in recent years (though with local decreases), and one might naturally wonder why. In a recent paper in Science, Nemani et al. ([2003]) focused on some of the potential causes of global vegetation growth during the past 20 years. Their analysis focused on only four variables: growing season average temperature, vapor pressure deficit, solar radiation, and net primary production (photosynthetic material). Their study considered only a limited variable set because of (a) the global scale of their analysis, and (b) the relatively long study period (18 years). Despite this limited scope (in terms of variables), their study gives substantial support to the hypothesis that the first three variables are causes of the last, and helps to clarify the functional form of those dependencies. At the same time, they explicitly note that there are many causally relevant variables that were ignored in their study, such as vegetation
Machine Learning and the Philosophy of Science: a Dynamic Interaction
 In Proceedings of the ECMLPKDD01 Workshop on Machine Leaning as Experimental Philosophy of Science
, 2001
"... I posit here a dynamic interaction between machine learning and the philosophy of science, and illustrate this claim with the use of a case study involving the foundations of Bayesian networks. ..."
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Cited by 4 (1 self)
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I posit here a dynamic interaction between machine learning and the philosophy of science, and illustrate this claim with the use of a case study involving the foundations of Bayesian networks.
A probability index of the robustness of a causal inference
 Journal of Educational and Behavioral Statistics
, 2004
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Primary and secondary hypohedonia
 Journal of Abnormal Psychology
, 2001
"... Having shown taxometrically that there exists a hypohedonic schizotypal taxon in a college population, J. J. ..."
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Cited by 3 (0 self)
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Having shown taxometrically that there exists a hypohedonic schizotypal taxon in a college population, J. J.
Bayesian Informal Logic and Fallacy
, 2002
"... Bayesian reasoning has been applied formally to statistical inference, machine learning and analyzing scientific method. Here I apply it informally to more common forms of inference, namely natural language arguments. I analyze a variety of traditional fallacies, deductive, inductive and causal, a ..."
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Cited by 3 (0 self)
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Bayesian reasoning has been applied formally to statistical inference, machine learning and analyzing scientific method. Here I apply it informally to more common forms of inference, namely natural language arguments. I analyze a variety of traditional fallacies, deductive, inductive and causal, and find more merit in them than is generally acknowledged. Bayesian principles provide a framework for understanding ordinary arguments which is well worth developing.
A dynamic interaction between machine learning and the philosophy of science
 Minds and Machines
, 2004
"... The relationship between machine learning and the philosophy of science can be classed as a dynamic interaction: a mutually beneficial connection between two autonomous fields that changes direction over time. I discuss the nature of this interaction and give a case study highlighting interactions b ..."
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Cited by 3 (1 self)
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The relationship between machine learning and the philosophy of science can be classed as a dynamic interaction: a mutually beneficial connection between two autonomous fields that changes direction over time. I discuss the nature of this interaction and give a case study highlighting interactions between research on Bayesian networks in machine learning and research on causality and probability in the philosophy of science.