Online research in philosophy

Researchers routinely face the problem of inferring causal relationships from large amounts of data, sometimes involving hundreds of variables. Often, it is the causal relationships between "latent" (unmeasured) variables that are of primary interest. The problem is how causal relationships between unmeasured variables can be inferred from measured data. For example, naval manpower researchers have been asked to infer the causal relations among psychological traits such as job satisfaction and job challenge from a data base in which neither trait is measured directly, but in which answers to interview questions are plausibly associated with each trait. By combining background knowledge with an algorithm that searches for causal structure among the unobserved variables, we have created a tool that can reliably extract useful causal information about latent variables from large data bases. In what follows we describe the class of causal models to which our..

In this paper I argue that the problem of mental causation can be solved by distinguishing between classificatory mental properties, like being a pain, and instances of those properties.Antireductive physicalism allows only that the former be irreducibly mental. Consequently, properties like being a pain cannot have causal commerce with the physical without violating causal closure. But instances of painfulness, according to the token identity thesis, are identical with various physical tokens and can therefore have causal efficacy in the physical world. Since we expect particular mental phenomena, not types or classes of mental phenomena to be involved in causal interactions, it is argued that antireductive physicalism can explain satisfactorily mental causation, despite the protests of Kim, Sosa, Honderich, and others. Being a mental state of a certain sort may have no causal efficacy, but the intentional and phenomenal properties of such states should, if my argument is correct.

This survey presents some of the main principles involved in discovering causal relations. They belong to a large array of possible assumptions and conditions about causal relations, whose various combinations limit the possibilities of acquiring causal knowledge in different ways. How much and in what detail the causal structure can be discovered from what kinds of data depends on the particular set of assumptions one is able to make. The assumptions considered here provide a starting point to explore further the foundations of causal discovery procedures, and how they can be improved.

To evaluate Hume's thesis that causal claims are always empirical, I consider three kinds of causal statement: ?e1 caused e2 ?, ?e1 promoted e2 ?, and ?e1 would promote e2 ?. Restricting my attention to cases in which ?e1 occurred? and ?e2 occurred? are both empirical, I argue that Hume was right about the first two, but wrong about the third. Standard causal models of natural selection that have this third form are a priori mathematical truths. Some are obvious, others less so. Empirical work on natural selection takes the form of defending causal claims of the first two types. I provide biological examples that illustrate differences among these three kinds of causal claim.

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.

Decision making typically requires judgements about causal relations: we need to know both the causal e¤ects of our actions and the causal relevance of various environmental factors. Judgements about the nature and strength of causal relations often di¤er, even among experts. How to handle such diversity is the topic of this paper. First we consider the possibility of aggregating causal judgements via the aggregation of probabilistic ones. The broadly negative outcome of this investigation leads us to look at aggregating causal judgements independently of probabilistic ones. We do so by transcribing causal claims into the judgement aggregation framework and applying some recent results in this …eld. Finally we look at the implications for probability aggregation when it is constrained by prior aggregation of causal judgements.

Causal realists maintain that the causal relation consists in something more than its relata. Specifying this relation in nonreductive terms is however notoriously difficult. Michael Tooley has advanced a plausible account avoiding some of the relation’s most obvious difficulties, particularly where these concern the notion of a cross-temporal connection. His account distinguishes discrete from nondiscrete causation, where the latter is suitable to the continuity of cross-temporal causation. I argue, however, that such accounts face conceptual difficulties dating from Zeno’s time. A Bergsonian resolution of these difficulties appears to entail that, for the causal realist, there can be no indirect causal relations of the sort envisioned by Tooley. A consequence of this discussion is that the causal realist must conceive all causal relations as ultimately direct.

This paper makes explicit and takes issue with the bizarre view, which is unfortunately prevalent among social scientists, that causal relations are features of models only. There are some good reasons to represent causal factors with independent variables. But the association between causes and independent variables is only a desideratum in model construction. It is not a criterion for judging which things are causes and which are effects.

Realist accounts of natural kinds rely on an account of causation where the relata of causal relations are real and discrete. These views about natural kinds entail very different accounts of causation. In particular, the necessity of the causal relation given the instantiation of the properties of natural kinds is more robust in the fundamental sciences (e.g. physics and chemistry) than it is in the life sciences (e.g. biology and the medical sciences). In this paper, I wish to argue that there is a difference in kind between the putative natural kinds of the fundamental sciences and those of the life sciences, such that a uniform account of causation cannot capture both. The upshot is that we must either reject the claim that the kinds of the life sciences are genuine natural kinds, or accept that there are different kinds of causal relations involving the relata of natural kinds. I accept the latter. I reject the objection that the true causal relations that relate macro-level kinds are to be found by “going down a level” to causal relation at the fundamental kind, because the relevant causal mechanisms are not at the fundamental level. Since, autonomous mechanistic accounts of causal relations at the macro-level can be provided (e.g. in Biology and medicine), I argue that realism about the natural kinds of the life sciences is justified. I address the problem of negative causation as a counterexample to the positive account of causation that is entailed by realism about natural kinds in the life sciences. I argue that an acceptance of realist accounts of two different kinds of natural kind makes a uniform analysis of causation look unpromising. (277 words).

Arguably no concept is more fundamental to science than that of causality, for investigations into cases of existence, persistence, and change in the natural world are largely investigations into the causes of these phenomena. Yet the metaphysics and epistemology of causality remain unclear. For example, the ontological categories of the causal relata have been taken to be objects (Hume 1739), events (Davidson 1967), properties (Armstrong 1978), processes (Salmon 1984), variables (Hitchcock 1993), and facts (Mellor 1995). (For convenience, causes and effects will usually be understood as events in what follows.) Complicating matters, causal relations may be singular (Socrates’s drinking hemlock caused Socrates’s death) or general (Drinking hemlock causes death); hence the relata might be tokens (e.g., instances of properties) or types (e.g., types of events) of the category in question. Other questions up for grabs are: Are singular causes metaphysically and/or epistemologically prior to general causes or vice versa (or neither)? What grounds the intuitive asymmetry of the causal relation? Are macro-causal relations reducible to micro-causal relations? And perhaps most importantly: Are causal facts (e.g., the holding of causal relations) reducible to non-causal facts (e.g., the holding of certain spatiotemporal relations)?