Clark Glymour

Halvorson argues through a series of examples and a general result due to Myers that the “semantic view” of theories has no available account of formal theoretical equivalence. De Bouvere provides criteria overlooked in Halvorson’s paper that are immune to his counterexamples and to the theorem he cites. Those criteria accord with a modest version of the semantic view that rejects some of Van Fraassen’s apparent claims while retaining the core of Patrick Suppes’s proposal. I do not endorse any version of the semantic view of theories.

We present evidence of a potentially serious source of error intrinsic to all spotted cDNA microarrays that use IMAGE clones of expressed sequence tags (ESTs). We found that a high proportion of these EST sequences contain 5V-end poly(dT) sequences that are remnants from the oligo(dT)-primed reverse transcription of polyadenylated mRNA templates used to generate EST cDNA for sequence clone libraries. Analysis of expression data from two single-dye cDNA microarray experiments showed that ESTs whose sequences contain repeats of consecutive 5V- end dT residues appeared to be strongly coexpressed, while expression data of all other sequences exhibited no such pattern. Our analysis suggests that expression data from sequences containing 5V poly(dT) tracts are more likely to be due to systematic cross-hybridization of these poly(dT) tracts than to true mRNA coexpression. This indicates that existing data generated by cDNA microarrays containing IMAGE clone ESTs should be filtered to remove expression data containing significant 5V poly(dT) tracts. D 2003 Elsevier Inc. All rights reserved.

is every bit as intelligible and philosophically respectable as many other doctrines currently in favor, e.g., the doctrine that mental events are identical with brain events; the attempt to give a linguistic construal of this latter doctrine meets many of the same sorts of difficulties encountered above (see Hempel, op. cit.). Secondly, I think that evidence for universal determinism may not, as a matter of fact, be so hard to come by as one might imagine. It is a striking fact about our world that we never observe any genuine cases of parallelism; it always seems possible to design some sort of interaction between any two genuine empirical magnitudes. If this is correct, then a true theory T can be deterministic only if universal determinism reigns

Few people have thought so hard about the nature of the quantum theory as has Jeff Bub,· and so it seems appropriate to offer in his honor some reflections on that theory. My topic is an old one, the consistency of our microscopic theories with our macroscopic theories, my example, the Aspect experiments (Aspect et al., 1981, 1982, 1982a; Clauser and Shimony, l978;_Duncan and Kleinpoppen, 199,8) is familiar, and my sirnplrcation of it is borrowed. All that is new here is a kind of diagonalization: an argument that the fundamental principles found to be violated by the quantum theory must be assumed to be true of the experimental apparatus used in the experiments..

A general principle for good pedagogic strategy is this: other things equal, make the essential principles of the subject explicit rather than tacit. We think that this principle is routinely violated in conventional instruction in statistics. Even though most of the early history of probability theory has been driven by causal considerations, the terms “cause” and “causation” have practically disappeared from statistics textbooks. Statistics curricula guide students away from the concept of causality, into remembering perhaps the cliche disclaimer “correlation does not mean causation,” but rarely thinking about what correlation does mean. The treatment of causality is a serious handicap to later studies of such topics as experimental design, where often the main goal is to establish (or disprove) causation. Much of the technical vocabulary of the language used in research design textbooks consists in euphemisms for specific causal relations, e.g, “latent variable,” “intervening variable,” “confounding factor,” etc. The multiplicity of terms used to refer to causation results in confusion and, in effect, may hinder understanding of the basic principles of research design.

Rather than attempting to characterize a relation of confirmation between evidence and theory, epistemology might better consider which methods of forming conjectures from evidence, or of altering beliefs in the light of evidence, are most reliable for getting to the truth. A logical framework for such a study was constructed in the early 1960s by E. Mark Gold and Hilary Putnam. This essay describes some of the results that have been obtained in that framework and their significance for philosophy of science, artificial intelligence, and for normative epistemology when truth is relative.

Through their transcript products genes regulate the rates at which an immense variety of transcripts and subsequent proteins occur. Understanding the mechanisms that determine which genes are expressed, and when they are expressed, is one of the keys to genetic manipulation for many purposes, including the development of new treatments for disease. Viewing each gene in a genome as a distinct variable that is either on or off, or more realistically as a continuous variable, the values of some of these variables influence the values of others through the regulatory proteins they express, including, of course, the possibility that the rate of expression of a gene at one time may, in various circumstances, influence the rate of expression of that same gene at a later time. If we imagine an arrow drawn from each gene expression variable at a given time to a gene variable whose expression it influences a short while after, the result is a network, technically a directed acyclic graph. For example, the DAG in Figure 1 is a representation of a system in which the expression level of gene G1 at time 1 ) causes the expression level of G2, which in turn causes the expression level of G3. The arrows in Figure 1 which do not have a variable at their tails are “error terms” which represent all of the causes of a variable other than the ones explicitly represented in the DAG. The DAG describes more than associations—it describes causal connections among gene expression rates. A shock to a cell—by mutation, heating, chemical treatment, etc. may alter the DAG describing the relations among gene expressions, for example by activating a gene that was otherwise not expressed, producing a cascade of new expression effects. Although “knockout” experiments can reveal some of the underlying causal network of gene expression levels, unless guided by information from other sources, such experiments are limited in how much of the network structure they can reveal, due to the sheer number of possible combinations of experimental manipulations of genes necessary to reveal the complete causal network. Recent developments have made it possible to compare quantitatively the expression of tens of thousands of genes in cells from different sources in a single experiment, and to trace gene expression over time in thousands of genes simultaneously. cDNA microarrays are already producing extensive data, much of it available on the web. Thus there are calls for analytic software that can be applied to microarray and other data to help infer regulatory networks. In this paper we will review current techniques that are available for searching for the causal relations between variables, describe algorithmic and data gathering obstacles to applying these techniques to gene expression levels, and describe the prospects for overcoming these obstacles

Recent literature in philosophy of science has addressed purported notions of explanatory virtues—‘explanatory power’, ‘unification’, and ‘coherence’. In each case, a probabilistic relation between a theory and data is said to measure the power of an explanation, or degree of unification, or degree of coherence. This essay argues that the measures do not capture cases that are paradigms of scientific explanation, that the available psychological evidence indicates that the measures do not capture judgements of explanatory power, and, finally, that the measures do not provide useful methods for selecting hypotheses. 1. Introduction2. Some Proposed Measures of Explanatory Virtues3. Descriptive Inadequacy3.1 Excellent but false explanations3.2 Causal explanation4. Psychological Inadequacy5. Finding the Truth6. Conclusion

The use of ceteris paribus clauses in philosophy and in the sciences has a long and fascinating history. Persky (1990) traces the use by economists of ceteris paribus clauses in qualifying generalizations as far back as William Petty’s Treatise of Taxes and Contributions (1662). John Cairnes’ The Character and Logical Method of Political Economy (1857) is credited with enunciating the idea that the conclusions of economic investigations hold “only in the absence of disturbing causes”.1 His Leading Principles (1874) contains the classic example of a ceteris paribus law: “The rate of wage, other things being equal, varies inversely with the supply of labour”. Carines’ ideas were popularized by Alfred Marshall in his Principles of Economics (1890) where he argued for a methodology that involved holding disturbing causes “in a pound called Caeteris Paribus”. It is unclear when the notion of ceteris paribus laws made its appearance in the philosophical literature; but in the nineteenth century it is to be found in Mill’s System of Logic (1843), and in the twentieth century it gained prominence in the Hempel-inspired debates of the 1950’s over the role of general laws in historical explanations, albeit under other labels such as quasi-laws (Rescher) or grounded generalizations (Scriven).