Michael Strevens

The concept of causation plays a central role in many philosophical theories, and yet no account of causation has gained widespread acceptance among those who have investigated its foundations. Theories based on laws, counterfactuals, physical processes, and probabilistic dependence and independence relations (the list is by no means exhaustive) have all received detailed treatment in recent years---{}and, while no account has been entirely successful, it is generally agreed that the concept has been greatly clari{}ed by the attempts. In this magni{}cent book, Woodward aims to give a uni{}ed account of causation and causal explanation in terms of the notion of a manipulation (or intervention, terms which can be read interchangeably). Not only does he produce in my view the most illuminating and comprehensive account of causation on o{}er, his theory also opens a great many avenues for future work in the area, and has rami{}cations for many other areas of philosophy. Making Things Happen ought to be of interest not only to philosophers of causation and philosophers of science, but to any philosopher whose concerns involve assumptions about the nature of causation, laws, or explanation

Research programs regularly compete to achieve the same goal, such as the discovery of the structure of DNA or the construction of a TEA laser. The more the competing programs share information, the faster the goal is likely to be reached, to society's benefit. But the "priority rule"—the scientific norm mandating that the first program to reach the goal in question receive all the credit for the achievement—provides a powerful disincentive for programs to share information. How, then, is the clash between social and self interest resolved in scientific practice? This paper investigates what Robert Merton called science's "communist" norm, which mandates universal sharing of knowledge, and uses mathematical models of discovery to argue that a communist regime may be on the whole advantageous and fair to all parties, and so might be implemented by a social contract that all scientists would be willing to sign.

According to principles of probability coordination, such as Miller's Principle or Lewis's Principal Principle, you ought to set your subjective probability for an event equal to what you take to be the objective probability of the event. For example, you should expect events with a very high probability to occur and those with a very low probability not to occur. This paper examines the grounds of such principles. It is argued that any attempt to justify a principle of probability coordination encounters the same difficulties as attempts to justify induction. As a result, no justification can be found.

This paper justifies the inference of probabilities from symmetries. I supply some examples of important and correct inferences of this variety. Two explanations of such inferences -- an explanation based on the Principle of Indifference and a proposal due to Poincaré and Reichenbach -- are considered and rejected. I conclude with my own account, in which the inferences in question are shown to be warranted a posteriori, provided that they are based on symmetries in the mechanisms of chance setups.

It is argued that the relation of instance confirmation has a role to play in scientific methodology that complements, rather than competing with, a modern account of inductive support such as Bayesian confirmation theory. When an instance confirms a hypothesis, it provides inductive support, but it also provides two things that other inductive supporters normally do not: first, a connection to “empirical data” that makes science epistemically special, and second, inductive support not only for the hypothesis as a whole, but for its parts. Further, when it is conceived in the right way, instance confirmation can duck the arguments most often thought to refute it. A causal account of instantiation, thus of instance confirmation, is offered that looks to deliver on all of the foregoing promises.

It seems very important to us whether or not a generalization offers counter-factual support—but why? Surely what happens in other possible worlds can neither help nor hurt us? This paper explores the question whether counter-factual support does, nevertheless, have some practical value. (The question of theoretical value will be addressed but then put aside.) The following thesis is proposed: the counterfactual-supporting generalizations are those for which there exists a compact and under normal circumstances knowable basis determining the fine-grained pattern of actual variation between the properties associated by the generalization (e.g., for the generalization Fs tend to be G, the exact circumstances under which any particular F is G); further, the better we understand the basis and the scope of the support offered, the greater our knowledge of fine-grained variation. We care about counterfactual support because we care about actual fine-grained variation

A recent paper by David Lewis, "Causation as Influence", proposes a new theory of causation. I argue against the theory, maintaining that (a) the relation asserted by a claim of the form "C was a cause of E" is distinct from the relation of causal influence, (b) the former relation depends very much, contra Lewis, on the individuation conditions for the event E, and (c) Lewis's account is unsatisfactory as an analysis of either kind of relation. The counterexamples presented here provide, I suggest, some insight into the reasons for the failure of counterfactual accounts of causal relations.

This paper offers a metaphysics of physical probability in (or if you prefer, truth conditions for probabilistic claims about) deterministic systems based on an approach to the explanation of probabilistic patterns in deterministic systems called the method of arbitrary functions. Much of the appeal of the method is its promise to provide an account of physical probability on which probability assignments have the ability to support counterfactuals about frequencies. It is argued that the eponymous arbitrary functions are of little philosophical use, but that they can be substituted for facts about frequencies without losing the ability to provide counterfactual support. The result is an account of probability in deterministic systems that has a “propensity-like” look and feel, yet which requires no supplement to the standard modern empiricist tool kit of particular matters of fact and principles of physical dynamics.

A theme of much work taking an ““economic approach”” to the study of science is the interaction between the norms of individual scientists and those of society at large. Though drawing from the same suite of formal methods, proponents of the economic approach offer what are in substantive terms profoundly different explanations of various aspects of the structure of science. The differences are illustrated by comparing Strevens's explanation of the scientific reward system (the ““priority rule””) with Max Albert's explanation of the prevalence of ““high methodological standards”” in science. Some objections to the economic approach as a whole are then briefly considered

Scientific understanding, this paper argues, can be analyzed entirely in terms of a mental act of “grasping” and a notion of explanation. To understand why a phenomenon occurs is to grasp a correct explanation of the phenomenon. To understand a scientific theory is to be able to construct, or at least to grasp, a range of potential explanations in which that theory accounts for other phenomena. There is no route to scientific understanding, then, that does not go by way of scientific explanation.

To understand the behavior of a complex system, you must understand the interactions among its parts. Doing so is difficult for non-decomposable systems, in which the interactions strongly influence the short-term behavior of the parts. Science's principal tool for dealing with non-decomposable systems is a variety of probabilistic analysis that I call EPA. I show that EPA's power derives from an assumption that appears to be false of non-decomposable complex systems, in virtue of their very non-decomposability. Yet EPA is extremely successful. I aim to find an interpretation of EPA's assumption that is consistent with, indeed that explains, its success.

Science is epistemically special, or so I will assume: it is better able to produce knowledge about the workings of the world than other knowledge-directed pursuits. Further, its superior epistemic powers are due to its being in some sense especially empirical: in particular, science puts great weight on a form of inductive reasoning that I call empirical con rmation. My aim in this paper is to investigate the nature of science’s “empiricism”, and to provide a preliminary explanation of the connection between empirical confirmation and epistemic efficacy. I will try to convince you that the place to find an account of empirical confirmation is the dusty, long-neglected instantialist account of scientific inference offered by mid-century logical empiricists. Some revision of instantialism will be required. As for what is advantageous in empirical confirmation, I propose that it is an unusual degree of independence from background belief.

Recent work on children’s inferences concerning biological and chemical categories has suggested that children (and perhaps adults) are essentialists— a view known as psychological essentialism. I distinguish three varieties of psychological essentialism and investigate the ways in which essentialism explains the inferences for which it is supposed to account. Essentialism succeeds in explaining the inferences, I argue, because it attributes to the child belief in causal laws connecting category membership and the possession of certain characteristic appearances and behavior. This suggests that the data will be equally well explained by a non-essentialist hypothesis that attributes belief in the appropriate causal laws to the child, but makes no claim as to whether or not the child represents essences. I provide several reasons to think that this non-essentialist hypothesis is in fact superior to any version of the essentialist hypothesis

What do the words "ceteris paribus" add to a causal hypothesis, that is, to a generalization that is intended to articulate the consequences of a causal mechanism? One answer, which looks almost too good to be true, is that a ceteris paribus hedge restricts the scope of the hypothesis to those cases where nothing undermines, interferes with, or undoes the effect of the mechanism in question, even if the hypothesis's own formulator is otherwise unable to specify fully what might constitute such undermining or interference. I will propose a semantics for causal generalizations according to which ceteris paribus hedges deliver on this promise, because the truth conditions for a causal generalization depend in part on the—perhaps unknown—nature of the mechanism whose consequences it is intended to describe. It follows that the truth conditions for causal hypotheses are typically opaque to the very scientists who formulate and test them.

Evolutionary biology distinguishes differences in survival and reproduction rates due to selection from those due to drift. The distinction is usually thought to be founded in probabilistic facts: a difference in (say) two variants' average lifespans over some period of time that is due to selection is explained by differences in the probabilities relevant to survival; in the purest cases of drift, by contrast, the survival probabilities are equal and the difference in lifespans is a matter of chance. When there is a difference in actual average lifespans there is always a difference in causal histories, but in drift, the causal differences make no contribution to the relevant probabilities. Some writers have claimed that the framework that determines which causes contribute to the probabilities, and so the framework that determines when the relevant probabilities differ and when they are equal, is imposed by modelers rather than being an objective fact of nature, and so that the distinction between selection and drift, however practically useful, is unreal. This paper uses my work on biological probabilities (Bigger than Chaos) and probabilistic explanation (Depth) to argue for the objectivity of, and the importance of the objectivity of, the distinction between selection and drift.

Cases of overdetermination or preemption continue to play an important role in the debate about the proper interpretation of causal claims of the form "C was a cause of E". I argue that the best treatment of preemption cases is given by Mackie's venerable INUS account of causal claims. The Mackie account suffers, however, from problems of its own. Inspired by its ability to handle preemption, I propose a dramatic revision to the Mackie account – one that Mackie himself would certainly have rejected – to solve these difficulties. The result is, I contend, a very attractive account of singular causal claims.

Science's priority rule rewards those who are first to make a discovery, at the expense of all other scientists working towards the same goal, no matter how close they may be to making the same discovery. I propose an explanation of the priority rule that, better than previous explanations, accounts for the distinctive features of the rule. My explanation treats the priority system, and more generally, any scheme of rewards for scientific endeavor, as a device for achieving an allocation of resources among different research programs that provides as much benefit as possible to society. I show that the priority system is especially well suited to finding an efficient allocation of resources in those situations, characteristic of scientific inquiry, in which any success in an endeavor subsequent to the first success brings little additional benefit to society.

Does the Bayesian theory of confirmation put real constraints on our inductive behavior? Or is it just a framework for systematizing whatever kind of inductive behavior we prefer? Colin Howson (Hume's Problem) has recently championed the second view. I argue that he is wrong, in that the Bayesian apparatus as it is usually deployed does constrain our judgments of inductive import, but also that he is right, in that the source of Bayesianism's inductive prescriptions is not the Bayesian machinery itself, but rather what David Lewis calls the ``Principal Principle''.

How to regard the weight we give to a proposition on the grounds of its being endorsed by an authority? I examine this question as it is raised within the epistemology of science, and I argue that “authority-based weight” should receive special handling, for the following reason. Our assessments of other scientists’ competence or authority are nearly always provisional, in the sense that to save time and money, they are not made nearly as carefully as they could be---indeed, they are typically made on the basis of only a small portion of the available evidence. Consequently, we need to represent the authority-based elements of our epistemic attitudes in such a way as to allow the later revision of those elements, in case we decide in the light of new priorities that a more conscientious assessment is warranted. I look to the literature in confirmation theory, statistics, and economics for a semiformal model of this revision process, and make a particular proposal of my own. The discussion also casts some light on the question of why certain aspects of science’s epistemic state are not made public

How can a model that stops short of representing the whole truth about the causal production of a phenomenon help us to understand the phenomenon? I answer this question from the perspective of what I call the simple view of understanding, on which to understand a phenomenon is to grasp a correct explanation of the phenomenon. Idealizations, I have argued in previous work, flag factors that are casually relevant but explanatorily irrelevant to the phenomena to be explained. Though useful to the would-be understander, such flagging is only a first step. Are there any further and more advanced ways that idealized models aid understanding? Yes, I propose: the manipulation of idealized models can provide considerable insight into the reasons that some causal factors are difference-makers and others are not, which helps the understander to grasp the nature of explanatory connections and so to better grasp the explanation itself

The system for awarding credit in science—the priority rule—functions, I have proposed elsewhere, to bring about something close to a socially optimal distribution of scientists among scientific research programs. If all goes well, then, potentially fruitful new ideas will be explored, unpromising ideas will be ignored, and fashionable but oversubscribed ideas will be deprived of further resources. Against this optimistic background, the present paper investigates the ways in which things might not go so well, that is, ways in which the priority rule might fail to realize its full potential as an incentive for scientists to work on the right things. Several possible causes of herding—an outcome in which a single research program ends up with a number of researchers well in excess of the optimum—are considered.

Why do we represent the world around us using causal generalizations, rather than, say, purely statistical generalizations? Do causal representations contain useful additional information, or are they merely more efficient for inferential purposes? This paper considers the second kind of answer: it investigates some ways in which causal cognition might aid us not because of its expressive power, but because of its organizational power. Three styles of explanation are considered. The first, building on the work of Reichenbach in "The Direction of Time", points to causal representation as especially efficient for predictive purposes in a world containing certain pervasive patterns of conditional independence. The second, inspired by work of Woodward and others, finds causal representation to be an excellent vehicle for representing all-important relations of manipulability. The third, based in part on my own work, locates the importance of causal cognition in the special role it reserves for information about underlying mechanisms. All three varieties of explanation show promise, but particular emphasis is placed on the third.

I aim to reconcile two apparently conflicting theses: (a) Everything that can be explained, can be explained in purely physical terms, that is, using the machinery of fundamental physics, and (b) some properties that play an explanatory role in the higher level sciences are irreducible in the strong sense that they are physically undefinable: their nature cannot be described using the vocabulary of physics. I investigate the contribution that physically undefinable properties typically make to explanations in the high-level sciences, and I show that when they are explanatorily relevant, it is in virtue of their extension (or something close) alone. They are irreducible because physics cannot capture their nature; this is no obstacle, however, to physics' more or less capturing their extension, which is all that it need do to duplicate their explanatory power. In the course of the argument, I sketch the outlines of an account of the explanation of physically contingent regularities, such as the regularities found in most branches of biological inquiry, at the center of which is an account of the nature of contingent, empirical bridge principles.

Hall has recently argued that there are two concepts of causality, picking out two different kinds of causal relation. McGrath, and Hitchcock and Knobe, have recently argued that the facts about causality depend on what counts as a “default” or “normal” state, or even on the moral facts. In the light of these claims you might be tempted to agree with Skyrms that causal relations constitute, metaphysically speaking, an “amiable jumble”, or with Cartwright that ‘causation’, though a single word, encompasses many different kinds of things. This paper argues, drawing on the author’s recent work on explanation, that the evidence adduced in support of causal pluralism can be accommodated easily by a unified theory of causality—a theory according to which all singular causal claims concern the same fundamental causal network