Clark Glymour

Words, Thoughts and Theories argues that infants and children discover the physical and psychological features of the world by a process akin to scientific inquiry, more or less as conceived by philosophers of science in the 1960s (the theory theory). This essay discusses some of the philosophical background to an alternative, more popular, “modular” or “maturational” account of development, dismisses an array of philosophical objections to the theory theory, suggests that the theory theory offers an undeveloped project for artificial intelligence, and, relying on recent psychological work on causation, offers suggestions about how principles of causal inference may provide a developmental solution to the “frame problem”.

sities. TETRAD II discovers a class of possible causal structures of a system from a data set containing measurements of the system variables. The signi cance of learning the causal structure of a system is that it allows for predicting the e ect of interventions into the system, crucial in policy making. Our data sets contained information on 204 U.S. national universities, collected by the US News and World Report magazine for the purpose of college ranking in 1992 and 1993. One apparently robust nding of our study is that student retention is directly related to the average standardized test scores of the incoming freshmen. When test scores of incoming students are controlled for, factors such as student faculty ratio, faculty salary, and university's educational expenses per student are all independent of graduation rates, and, therefore, do not seem to directly in uence student retention. As the test scores are indicators of the overall quality of the incoming students, we predict that one of the most e ective ways of improving student retention in an individual university is increasing the university's selectivity.

For most of the contributions to this volume, the project is this: Fill out “Event X is a cause of event Y if and only if……” where the dots on the right are to be filled in by a claims formulated in terms using any of (1) descriptions of possible worlds and their relations; (2) a special predicate, “is a law;” (3) “chances;” and (4) anything else one thinks one needs. The form of analysis is roughly the same as that sought in the Meno, and the methodology is likewise Socratic—proposals, examples, counterexamples, more proposals. The norms of the enterprise seem to be as follows (i) a proposal is defeated if someone can imagine a circumstance in which it would be false, or perhaps if one can imagine such a circumstance that is not obviously inconsistent with physical laws; (ii) approximately correct solutions, those which cover most but not all cases, are of no value unless they can be modified to cover all cases; (iii) no account is required of how the relations in the right hand side of a proposed analysis could be known or reliably..

We consider the dispute between causal decision theorists and evidential decision theorists over Newcomb-like problems. We introduce a framework relating causation and directed graphs developed by Spirtes et al. (1993) and evaluate several arguments in this context. We argue that much of the debate between the two camps is misplaced; the disputes turn on the distinction between conditioning on an event E as against conditioning on an event I which is an action to bring about E. We give the essential machinery for calculating the effect of an intervention and consider recent work which extends the basic account given here to the case where causal Knowledge is incomplete.

& Carnegie Mellon University Abstract The rationality of human causal judgments has been the focus of a great deal of recent research. We argue against two major trends in this research, and for a quite different way of thinking about causal mechanisms and probabilistic data. Our position rejects a false dichotomy between "mechanistic" and "probabilistic" analyses of causal inference -- a dichotomy that both overlooks the nature of the evidence that supports the induction of mechanisms and misses some important probabilistic implications of mechanisms. This dichotomy has obscured an alternative conception of causal learning: for discrete events, a central adaptive task is to induce causal mechanisms in the environment from probabilistic data and prior knowledge. Viewed from this perspective, it is apparent that the probabilistic norms assumed in the human causal judgment literature often do not map onto the mechanisms generating the probabilities. Our alternative conception of causal judgment is more congruent with both scientific uses of the notion of causation and observed causal judgments of untutored reasoners. We illustrate some of the relevant variables under this conception, using a framework for causal representation now widely adopted in computer science and, increasingly, in statistics. We also review the formulation and evidence for a theory of human causal induction (Cheng, 1997) that adopts this alternative conception.

"Goodness of Fit": Clinical Applications from Infancy through Adult Life. By Stella Chess & Alexander Thomas. Brunner/Mazel, Philadelphia, PA, 1999. pp. 229. pound24.95 (hb). Chess and Thomas's pioneering longitudinal studies of temperamental individuality started over 40 years ago (Thomas et al., 1963). Their publications soon became and remain classics. Their concept of "goodness of fit" emerges out of this monumental work but has had a long gestation period. In their new book, the authors distinguish between behaviour disorders that are reactive to the child's life circumstances, including life events, and which are self-correcting or responsive to the relevant changes in their environment, and more serious disorders

Reverse inference in cognitive neuropsychology has been characterized as inference to ‘psychological processes’ from ‘patterns of activation’ revealed by functional magnetic resonance or other scanning techniques. Several arguments have been provided against the possibility. Focusing on Machery’s presentation, we attempt to clarify the issues, rebut the impossibility arguments, and propose and illustrate a strategy for reverse inference. 1 The Problem of Reverse Inference in Cognitive Neuropsychology2 The Arguments2.1 The anti-Bayesian argument3 Patterns of Activation4 Reverse Inference Practiced5 Seek and Ye Shall Find, Maybe6 Conclusion

Time series of macroscopic quantities that are aggregates of microscopic quantities, with unknown one‐many relations between macroscopic and microscopic states, are common in applied sciences, from economics to climate studies. When such time series of macroscopic quantities are claimed to be causal, the causal relations postulated are representable by a directed acyclic graph and associated probability distribution—sometimes called a dynamical Bayes net. Causal interpretations of such series imply claims that hypothetical manipulations of macroscopic variables have unambiguous effects on variables “downstream” in the graph, and such macroscopic variables may be predictably produced or altered even while particular microstates are not. This paper argues that such causal time series of macroscopic aggregates of microscopic processes are the appropriate model for mental causation.