Clark Glymour

Twenty years ago, Nancy Cartwright wrote a perceptive essay in which she clearly distinguished causal relations from associations, introduced philosophers to Simpson’s paradox, articulated the difficulties for reductive probabilistic analyses of causation that flow from these observations, and connected causal relations with strategies of action (Cartwright 1979). Five years later, without appreciating her essay, I and my (then) students began to develop formal representations of causal and probabilistic relations, which, subsequently informed by the work of computer scientists and statisticians, led eventually to a practical theory of causal inference and prediction, a theory incorporating some of the sensibilities Cartwright had voiced (Glymour et al. 1987; Spirtes et al. 1993). That theory, and ideas related to it, have become a subfield of computer science with contributions far deeper than mine from many sources, and its inferential and predictive techniques have been successfully applied in biology, economics, educational research, geology and space physics.

Convergent realists desire scientific methods that converge reliably to informative, true theories over a wide range of theoretical possibilities. Much attention has been paid to the problem of induction from quantifier-free data. In this paper, we employ the techniques of formal learning theory and model theory to explore the reliable inference of theories from data containing alternating quantifiers. We obtain a hierarchy of inductive problems depending on the quantifier prefix complexity of the formulas that constitute the data, and we provide bounds relating the quantifier prefix complexity of the data to the quantifier prefix complexity of the theories that can be reliably inferred from such data without background knowledge. We also examine the question whether there are theories with mixed quantifiers that can be reliably inferred with closed, universal formulas in the data, but not without

Contemporary cognitive neuropsychology attempts to infer unobserved features of normal human cognition, or ‘cognitive architecture’, from experiments with normals and with brain-damaged subjects in whom certain normal cognitive capacities are altered, diminished, or absent. Fundamental methodological issues about the enterprise of cognitive neuropsychology concern the characterization of methods by which features of normal cognitive architecture can be identified from such data, the assumptions upon which the reliability of such methods are premised, and the limits of such methods—even granting their assumptions—in resolving uncertainties about that architecture. With some idealization, the question of the capacities of various experimental designs in cognitive neuropsychology to uncover cognitive architecture can be reduced to comparatively simple questions about the prior assumptions investigators are willing to make. This paper presents some of simplest of those reductions.

Words, Thoughts and Theories arguesthat infants and children discover the physical and psychological featuresof the world by a process akin to scientific inquiry, more or less asconceived by philosophers of science in the 1960s (the theory theory).This essay discusses some of the philosophical background to analternative, more popular, ``modular'''' or ``maturational'''' account ofdevelopment, dismisses an array of philosophical objections to the theorytheory, suggests that the theory theory offers an undeveloped project forartificial intelligence, and, relying on recent psychological work oncausation, offers suggestions about how principles of causal inference mayprovide a developmental solution to the ``frame problem''''.