James Woodward

This paper explores the interpretation and use of polygenic risk scores (PRSs). We argue that PRSs generally do not directly embody causal information. Nonetheless, they can assist us in tracking other causal relationships concerning genetic effects. Although their purely predictive/correlational use is important, it is this tracking feature that contributes to their potential usefulness in other applications, such as genetic dissection, and their use as controls, which allow us, indirectly, to "see" more clearly the role of environmental variables.

What is the relationship between, on the one hand, the sorts of causal claims found in the special sciences (and in common sense) and, on the other hand, the world as described by physics? A standard picture goes like this: the fundamental laws of physics are causal laws in the sense that they can be interpreted as telling us that realizations of one set of physical factors or properties “causes” realizations of other properties. Causal claims in the special sciences are then true (to the extent that they are) in virtue of “instantiating” these underlying causal laws; as it is often put, the latter serve as “truth-makers” for the former. The picture is thus one according to which the notion of cause, as it occurs in the special sciences, is reflected or “grounded” in a fairly straightforward and transparent way in a similar notion that occurs in fundamental physics. This paper explores some alternatives to this picture.

This paper makes use of recent empirical results, mainly from experimental economics, to expore the conditions under which people will cooperate and to assess competing explantions of this cooperation. It is argued that the evidence supports the claim that people differ in type, with some being conditional cooperators and others being motivated by more or less sophisticated forms of self-interest. Stable cooperation requires, among other things, rules and institutions that protect conditional cooperators from myopically self-interested types. Additional empirical features of the behavior of conditional cooperators also imply that rules and institutions are required to produce stable cooperation.

The Faradayan hypothesis of inductive coupling of the electromagnetic and gravitational fields is briefly discussed. An experiment designed to test the hypothesis wherein samples are spun to see if any electrogravitational charge is induced is described. Results of the experiment are reported. They imply the induction of a charge density ρ* for spinning samples that behaves as ρ*=βρma, where ρm is the mass density of an element of matter experiencing an acceleration a, and β is the coupling coefficient for the hypothetical electrogravitational induction effect. In this experiment, β is found to have the value(9.6±3.3)×10 −13 statcoulombs/dyne. Tests that seem to rule out explanations of the observed charges in terms of conventional charging mechanisms are considered

This is a wonderful book; indeed, it is easily one of the most important and creative books I have ever read on the subject of causation and causal inference. Causality is impressive on many levels and should be of great interest to many different audiences. Philosophers will find of particular interest Pearl's defense of what might be described as a broadly manipulationist or interventionist treatment of causation: Causal claims have to do with what would happen under ideal, suitably surgical experimental manipulations . But Pearl moves far beyond philosophical generalities on this theme: He makes the case for the approach that he favors by developing a formal apparatus for talking about interventions and how they relate to causation and information about probabilities that is at once both intuitively compelling and genuinely useful for purposes of calculation and estimation. He also actively engages the philosophical literature on causation at many points – for example, there are illuminating discussions of evidential decision theory, of probabilistic theories of causation of the sort favored by Eells and Cartwright, of Lewisian counterfactual theories of causation, and of the relationship between so-called type and token causal claims, as well as a quite novel treatment of the latter

Hierarchical Bayesian models (HBMs) provide an account of Bayesian inference in a hierarchically structured hypothesis space. Scientific theories are plausibly regarded as organized into hierarchies in many cases, with higher levels sometimes called ‘paradigms’ and lower levels encoding more specific or concrete hypotheses. Therefore, HBMs provide a useful model for scientific theory change, showing how higher‐level theory change may be driven by the impact of evidence on lower levels. HBMs capture features described in the Kuhnian tradition, particularly the idea that higher‐level theories guide learning at lower levels. In addition, they help resolve certain issues for Bayesians, such as scientific preference for simplicity and the problem of new theories. *Received July 2009; revised October 2009. †To contact the authors, please write to: Leah Henderson, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 32D‐808, Cambridge, MA 02139; e‐mail: [email protected].

How do people judge the degree of causal responsibility that an agent has for the outcomes of her actions? We show that a relatively unexplored factor -- the robustness of the causal chain linking the agent’s action and the outcome -- influences judgments of causal responsibility of the agent. In three experiments, we vary robustness by manipulating the number of background circumstances under which the action causes the effect, and find that causal responsibility judgments increase with robustness. In the first experiment, the robustness manipulation also raises the probability of the effect given the action. Experiments 2 and 3 control for probability-raising, and show that robustness still affects judgments of causal responsibility. In particular, Experiment 3 introduces an Ellsberg type of scenario to manipulate robustness, while keeping the conditional probability and the skill deployed in the action fixed. Experiment 4, replicates the results of Experiment 3, while contrasting between judgments of causal strength and of causal responsibility. The results show that in all cases, the perceived degree of responsibility increases with the robustness of the action-outcome causal chain.

This paper explores some issues concerning how we should think about interventions (in the sense of unconfounded manipulations) of "upper-level" variables in contexts in which these supervene on but are not identical with lower-level realizers. It is argued that we should reject the demand that interventions on upper-level variables must leave their lower-level realizers unchanged– a requirement that within an interventionist framework would imply that upper-level variables are causally inert. Instead an intervention on an upper-level variable at the same time changes its lower-level realizer in a way that is consistent with the change in the upper- level variable. The lower-level realizer should not be regarded as a potential "confounder" of upper-level causal relations, as in "exclusionist" arguments. Several proposals for making this precise are considered and several pro-exclusionist arguments are criticized.

Following Jacques Hadamard, applied mathematicians typically investigate their models in the form of well-set problems, which actually consist of a family of applicational circumstances that vary in specific ways with respect to their initial and boundary values. The chief motive for investigating models in this wider manner is to avoid the improper behavioral conclusions one might reach from the consideration of a more restricted range of cases. Suitable specifications of the required initial and boundary variability typically appeal to previously established experimental conclusions as to how the target system will behave under a range of eternally applied manipulations of the form “If the conditions pertaining to S were altered in manner M, internal features X would/would not alter”. In his investigations of causal reasoning within other parts of science, our first author has emphasized the conceptual importance of counterfactuals of this nature, for which he was been often criticized by authors of a self-styled “metaphysical” inclination. The purpose of this note is to argue, pace these objections, that closely analogous considerations have long been part of the practice of investigating differential equation models in a sensible way.

Counterfactuals all the way down? Content Type Journal Article DOI 10.1007/s11016-010-9437-9 Authors Jim Woodward, History and Philosophy of Science, 1017 Cathedral of Learning, University of Pittsburgh, Pittsburgh, PA 15260, USA Barry Loewer, Department of Philosophy, Rutgers University, New Brunswick, NJ 08901, USA John W. Carroll, Department of Philosophy and Religious Studies, North Carolina State University, Raleigh, NC 27695-8103, USA Marc Lange, Department of Philosophy, University of North Carolina at Chapel Hill, CB#3125—Caldwell Hall, Chapel Hill, NC 27599-3125, USA Journal Metascience Online ISSN 1467-9981 Print ISSN 0815-0796 Journal Volume Volume 20 Journal Issue Volume 20, Number 1