We argue that current discussions of criteria for actual causation are ill-posed in several respects. (1) The methodology of current discussions is by induction from intuitions about an infinitesimal fraction of the possible examples and counterexamples; (2) cases with larger numbers of causes generate novel puzzles; (3) "neuron" and causal Bayes net diagrams are, as deployed in discussions of actual causation, almost always ambiguous; (4) actual causation is (intuitively) relative to an initial system state since state changes are relevant, but (...) most current accounts ignore state changes through time; (5) more generally, there is no reason to think that philosophical judgements about these sorts of cases are normative; but (6) there is a dearth of relevant psychological research that bears on whether various philosophical accounts are descriptive. Our skepticism is not directed towards the possibility of a correct account of actual causation; rather, we argue that standard methods will not lead to such an account. A different approach is required. (shrink)

I argue that a phenotypic trait can be an adaptation to a particular environmental condition, as against others, only if the environmental condition and the phenotype interactively cause fitness. Models of interactive environmental causes of fitness generally require that environments be individuated by explicit representation rather than by measures of environmental quality, although the latter understanding of ‘environment’ is more prominent in the philosophy of biology. Hence, talk of adaptations to some but not other environmental conditions relies on conceptions of (...) ‘environment’ importantly different from that commonly presupposed in philosophy of biology. (shrink)

Since the introduction of mathematical population genetics, its machinery has shaped our fundamental understanding of natural selection. Selection is taken to occur when differential fitnesses produce differential rates of reproductive success, where fitnesses are understood as parameters in a population genetics model. To understand selection is to understand what these parameter values measure and how differences in them lead to frequency changes. I argue that this traditional view is mistaken. The descriptions of natural selection rendered by population genetics models are (...) in general neither predictive nor explanatory and introduce avoidable conceptual confusions. I conclude that a correct understanding of natural selection requires explicitly causal models of reproductive success. *Received May 2006; revised December 2006. †To contact the author, please write to: Department of Philosophy, Kansas State University, 201 Dickens Hall, Manhattan, KS 66506; e‐mail: [email protected] . (shrink)

I argue that results from foraging theory give us good reason to think some evolutionary phenomena are indeterministic and hence that evolutionary theory must be probabilistic. Foraging theory implies that random search is sometimes selectively advantageous, and experimental work suggests that it is employed by a variety of organisms. There are reasons to think such search will sometimes be genuinely indeterministic. If it is, then individual reproductive success will also be indeterministic, and so too will frequency change in populations of (...) organisms employing such search. (shrink)

In this article I explore some statistical difficulties confronting going conceptions of ‘group’ as understood in accounts of group selection. Most such theories require real groups but define the reality of groups in ways that make it impossible to test for their reality. There are alternatives, but they either require or invite a nominalism about groups that many theorists abjure.

Standard models of statistical explanation face two intractable difficulties. In his 1984 Salmon argues that because statistical explanations are essentially probabilistic we can make sense of statistical explanation only by rejecting the intuition that scientific explanations are contrastive. Further, frequently the point of a statistical explanation is to identify the etiology of its explanandum, but on standard models probabilistic explanations often fail to do so. This paper offers an alternative conception of statistical explanations on which explanations of the frequency of (...) a property consist in the derivation of that frequency from a statistical specification of the mechanism by which instances of the relevant property are produced. Such explanations are contrastive precisely because they identify the determinate causal etiologies of their explananda. (shrink)

Focused correlation compares the degree of association within an evidence set to the degree of association in that evidence set given that some hypothesis is true. Wheeler and Scheines have shown that a difference in incremental confirmation of two evidence sets is robustly tracked by a difference in their focus correlation. In this essay, we generalize that tracking result by allowing for evidence having unequal relevance to the hypothesis. Our result is robust as well, and we retain conditions for bidirectional (...) tracking between incremental confirmation measures and focused correlation. (shrink)

Measures of algorithmic bias can be roughly classified into four categories, distinguished by the conditional probabilistic dependencies to which they are sensitive. First, measures of "procedural bias" diagnose bias when the score returned by an algorithm is probabilistically dependent on a sensitive class variable (e.g. race or sex). Second, measures of "outcome bias" capture probabilistic dependence between class variables and the outcome for each subject (e.g. parole granted or loan denied). Third, measures of "behavior-relative error bias" capture probabilistic dependence between (...) class variables and the algorithmic score, conditional on target behaviors (e.g. recidivism or loan default). Fourth, measures of "score-relative error bias" capture probabilistic dependence between class variables and behavior, conditional on score. Several recent discussions have demonstrated a tradeoff between these different measures of algorithmic bias, and at least one recent paper has suggested conditions under which tradeoffs may be minimized. -/- In this paper we use the machinery of causal graphical models to show that, under standard assumptions, the underlying causal relations among variables forces some tradeoffs. We delineate a number of normative considerations that are encoded in different measures of bias, with reference to the philosophical literature on the wrongfulness of disparate treatment and disparate impact. While both kinds of error bias are nominally motivated by concern to avoid disparate impact, we argue that consideration of causal structures shows that these measures are better understood as complicated and unreliable measures of procedural biases (i.e. disparate treatment). Moreover, while procedural bias is indicative of disparate treatment, we show that the measure of procedural bias one ought to adopt is dependent on the account of the wrongfulness of disparate treatment one endorses. Finally, given that neither score-relative nor behavior-relative measures of error bias capture the relevant normative considerations, we suggest that error bias proper is best measured by score-based measures of accuracy, such as the Brier score. (shrink)

argues that correlated interactions are necessary for group selection. His argument turns on a particular procedure for measuring the strength of selection, and employs a restricted conception of correlated interaction. It is here shown that the procedure in question is unreliable, and that while related procedures are reliable in special contexts, they do not require correlated interactions for group selection to occur. It is also shown that none of these procedures, all of which employ partial regression methods, are reliable when (...) correlated interactions of a specific kind arise, and it is argued that such correlated interactions will likely be ubiquitous in natural populations. Introduction Process and Product Fitness, Mean Fitness, and Phenotypic Change Correlated Interactions Causation Implications CiteULike Connotea Del.icio.us What's this? (shrink)

: Models that fail to satisfy the Markov condition are unstable in the sense that changes in state variable values may cause changes in the values of background variables, and these changes in background lead to predictive error. This sort of error arises exactly from the failure of non-Markovian models to track the set of causal relations upon which the values of response variables depend. The result has implications for discussions of the level of selection: under certain plausible conditions the (...) models of selection presented in such debates will not satisfy the Markov condition when fit to data from real populations. Since this is true both for group and individual level models, models of neither sort correctly represent the causal structure generating, nor correctly explain, the phenomena of interest. (shrink)

Sober (1984) presents an account of selection motivated by the view that one property can causally explain the occurrence of another only if the first plays a unique role in the causal production of the second. Sober holds that a causal property will play such a unique role if it is a population level cause of its effect, and on this basis argues that there is selection for a trait T only if T is a population level cause of survival (...) and reproductive success. Sterelny and Kitcher (1988) claim against Sober that some traits directly subject to selection will not satisfy the probabilistic condition on population level causation. In this paper I show that Sober has the resources to resist the Sterelny-Kitcher complaint, but I argue that not all traits that satisfy the probabilistic condition play the required unique role in the production of their effects. (shrink)

I argue that the orthodox account of probabilistic causation, on which probabilistic causes determine the probability of their effects, is inconsistent with certain ontological assumptions implicit in scientific practice. In particular, scientists recognize the possibility that properties of populations can cause the behavior of members of the populations. Such emergent population‐level causation is metaphysically impossible on the orthodoxy.

Glymour (Philos Sci 73:369–389, 2006) claims that classical population genetic models can reliably predict short and medium run population dynamics only given information about future fitnesses those models cannot themselves predict, and that in consequence the causal, ecological models which can predict future fitnesses afford a more foundational description of natural selection than do population genetic models. This paper defends the first claim from objections offered by Gildenhuys (Biol Philos, 2011).

Lennox and Wilson (1994) critique dispositional accounts of selection on the grounds that such accounts will class evolutionary events as cases of selection whether or not the environment constrains population growth. Lennox and Wilson claim that pure r-selection involves no environmental checks on growth, and that accounts of natural selection ought to distinguish between the two sorts of cases. I argue that Lennox and Wilson are mistaken in claiming that pure r-selection involves no environmental checks, but suggest that two related (...) cases support their substantive complaint, namely that dispositional accounts of selection have resources insufficient for making important distinctions in causal structure. (shrink)