Really statistical explanation is a hitherto neglected form of noncausal scientific explanation. Explanations in population biology that appeal to drift are RS explanations. An RS explanation supplies a kind of understanding that a causal explanation of the same result cannot supply. Roughly speaking, an RS explanation shows the result to be mere statistical fallout.

This article compares causal and constitutive explanation. While scientific inquiry usually addresses both causal and constitutive questions, making the distinction is crucial for a detailed understanding of scientific questions and their interrelations. These explanations have different kinds of explananda and they track different sorts of dependencies. Constitutive explanations do not address events or behaviors, but causal capacities. While there are some interesting relations between building and causal manipulation, causation and constitution are not to be confused. Constitution is a synchronous and (...) asymmetric relation between relata that cannot be conceived as independent existences. However, despite their metaphysical differences, the same key ideas about explanation largely apply to both. Causal and constitutive explanations face similar challenges (such as the problems of relevance and explanatory regress) and both are in the business of mapping networks of counterfactual dependence—i.e. mechanisms—although the relevant counterfactuals are of a different sort. In the final section the issue of developmental explanation is discussed. It is argued that developmental explanations deserve their own place in taxonomy of explanations, although ultimately developmental dependencies can be analyzed as combinations of causal and constitutive dependencies. Hence, causal and constitutive explanation are distinct, but not always completely separate forms of explanation. (shrink)

This paper attempts to elucidate three characteristics of causal relationships that are important in biological contexts. Stability has to do with whether a causal relationship continues to hold under changes in background conditions. Proportionality has to do with whether changes in the state of the cause “line up” in the right way with changes in the state of the effect and with whether the cause and effect are characterized in a way that contains irrelevant detail. Specificity is connected both to (...) David Lewis’ notion of “influence” and also with the extent to which a causal relation approximates to the ideal of one cause–one effect. Interrelations among these notions and their possible biological significance are also discussed. (shrink)

Waters’s (2007) actual difference making and Weber’s (2013, 2017) biological normality approaches to causal selection have received many criticisms, some of which miss their target. Disagreement about whether Waters’s and Weber’s views succeed in providing criteria that uniquely singles out the gene as explanatorily significant in biology has led philosophers to overlook a prior problem. Before one can address whether Waters’s and Weber’s views successfully account for the explanatory significance of genes, one must ask whether either view satisfactorily meets the (...) necessary conditions for causal selection in the first place. An adequate defense of causal selection must meet two desiderata. First, there must be an explanatory property that sets some causes apart from others. Second, the property identified must be one that is recognized by biologists as relevant to their domain(s) of inquiry. I argue that both fall short of meeting the second condition. I demonstrate this by showing how many of the biological technologies crucial to experimentation do not fit either view very well. I offer a more adequate proposal that accommodates non-actual and artificial causal variables. A consequence of my view is the following: When analyzing the causal selection practices of biologists, philosophers should consider the explanatory targets relevant to a research program – including ones whose explanans must appeal to biological technologies. I then explain how this proposal can inform the existing debate between Weber (2017) and Griffiths et al. (2015). (shrink)

Causal selection has to do with the distinction we make between background conditions and “the” true cause or causes of some outcome of interest. A longstanding consensus in philosophy views causal selection as lacking any objective rationale and as guided, instead, by arbitrary, pragmatic, and non-scientific considerations. I argue against this position in the context of causal selection for disease traits. In this domain, causes are selected on the basis of the type of causal control they exhibit over a disease (...) of interest. My analysis clarifies the principled rationale that guides this selection and how it involves both pragmatic and objective considerations, which have been overlooked in the extant literature. (shrink)

In the past century, nearly all of the biological sciences have been directly affected by discoveries and developments in genetics, a fast-evolving subject with important theoretical dimensions. In this rich and accessible book, Paul Griffiths and Karola Stotz show how the concept of the gene has evolved and diversified across the many fields that make up modern biology. By examining the molecular biology of the 'environment', they situate genetics in the developmental biology of whole organisms, and reveal how the molecular (...) biosciences have undermined the nature/nurture distinction. Their discussion gives full weight to the revolutionary impacts of molecular biology, while rejecting 'genocentrism' and 'reductionism', and brings the topic right up to date with the philosophical implications of the most recent developments in genetics. Their book will be invaluable for those studying the philosophy of biology, genetics and other life sciences. (shrink)

A number of writers, myself included, have recently argued that an “interventionist” treatment of causation of the sort defended in Woodward, 2003 can be used to cast light on so-called “causal exclusion” arguments. This interventionist treatment of causal exclusion has in turn been criticized by other philosophers. This paper responds to these criticisms. It describes an interventionist framework for thinking about causal relationships when supervenience relations are present. I contend that this framework helps us to see that standard arguments for (...) causal exclusion involve mistaken assumptions about what it is appropriate to control for or hold fixed in assessing causal claims. The framework also provides a natural way of capturing the idea that properties that supervene on but that are not identical with realizing properties can be causally efficacious. (shrink)

According to a causal model of explanation, we explain phenomena by giving their causes or, where the phenomena are themselves causal regularities, we explain them by giving a mechanism linking cause and effect. If we explain why smoking causes cancer, we do not give the cause of this causal connection, but we do give the causal mechanism that makes it. The claim that to explain is to give a cause is not only natural and plausible, but it also avoids many (...) of the objections to other accounts of explanation, such as the views that to explain is to give a reason to believe the phenomenon occurred, to somehow make the phenomenon familiar, or to give a Deductive-Nomological argument. Unlike the reason for belief account, a causal model makes a clear distinction between understanding why a phenomenon occurs and merely knowing that it does, and the model does so in a way that makes understanding unmysterious and objective. Understanding is not some sort of super-knowledge, but simply more knowledge: knowledge of the phenomenon and knowledge of its causal history. A causal model makes it clear how something can explain without itself being explained, and so avoids the regress of whys, since we can know a phenomenon's cause without knowing the cause of the cause. It also accounts for legitimate self-evidencing explanations, explanations where the phenomenon is an essential part of the evidence that the explanation is correct, so the explanation can not supply a non-circular reason for believing the phenomenon occurred. There is no barrier to knowing a cause through its effects and also knowing that it is their cause. The speed of recession of a star explains its observed red-shift, even though the shift is an essential part of the evidence for its speed of recession. The model also avoids the most serious objection to the familiarity view, which is that some phenomena are familiar yet not understood, since a phenomenon can be perfectly familiar, such as the blueness of the sky or the fact that the same side of the moon always faces the earth, even if we do not know its cause. Finally, a causal model avoids many of the objections to the Deductive-Nomological model. Ordinary explanations do not have to meet the requirements of the Deductive-Nomological model, because one does not need to give a law to give a cause, and one does not need to know a law to have good reason to believe that a cause is a cause. As for the notorious over-permissiveness of the Deductive-Nomological model, the reason recession explains red-shift but not conversely, is simply that causes explain effects but not conversely, and the reason a conjunction of laws does not explain its conjuncts is that conjunctions do not cause their conjuncts. (shrink)

Certain scientific explanations of physical facts have recently been characterized as distinctively mathematical –that is, as mathematical in a different way from ordinary explanations that employ mathematics. This article identifies what it is that makes some scientific explanations distinctively mathematical and how such explanations work. These explanations are non-causal, but this does not mean that they fail to cite the explanandum’s causes, that they abstract away from detailed causal histories, or that they cite no natural laws. Rather, in these explanations, (...) the facts doing the explaining are modally stronger than ordinary causal laws or are understood in the why question’s context to be constitutive of the physical arrangement at issue. A distinctively mathematical explanation works by showing the explanandum to be more necessary than ordinary causal laws could render it. Distinctively mathematical explanations thus supply a kind of understanding that causal explanations cannot. 1 Introduction2 Some Distinctively Mathematical Scientific Explanations3 Are Distinctively Mathematical Explanations Set Apart by their Failure to Cite Causes? 4 Distinctively Mathematical Explanations do not Exploit Causal Powers5 How these Distinctively Mathematical Explanations Work6 Conclusion. (shrink)

Bogen and Woodward claim that the function of scientific theories is to account for 'phenomena', which they describe both as investigator-independent constituents of the world and as corresponding to patterns in data sets. I argue that, if phenomena are considered to correspond to patterns in data, it is inadmissible to regard them as investigator-independent entities. Bogen and Woodward's account of phenomena is thus incoherent. I offer an alternative account, according to which phenomena are investigator-relative entities. All the infinitely many patterns (...) that data sets exhibit have equal intrinsic claim to the status of phenomenon: each investigator may stipulate which patterns correspond to phenomena for him or her. My notion of phenomena accords better both with experimental practice and with the historical development of science. (shrink)

Biologists studying complex causal systems typically identify some factors as causes and treat other factors as background conditions. For example, when geneticists explain biological phenomena, they often foreground genes and relegate the cellular milieu to the background. But factors in the milieu are as causally necessary as genes for the production of phenotypic traits, even traits at the molecular level such as amino acid sequences. Gene-centered biology has been criticized on the grounds that because there is parity among causes, the (...) “privileging” of genes reflects a reductionist bias, not an ontological difference. The idea that there is an ontological parity among causes is related to a philosophical puzzle identified by John Stuart Mill: what, other than our interests or biases, could possibly justify identifying some causes as the actual or operative ones, and other causes as mere background? The aim of this paper is to solve this conceptual puzzle and to explain why there is not an ontological parity among genes and the other factors. It turns out that solving this puzzle helps answer a seemingly unrelated philosophical question: what kind of causal generality matters in biology? (shrink)

It is usually supposed that the central limit theorem explains why various quantities we find in nature are approximately normally distributed—people's heights, examination grades, snowflake sizes, and so on. This sort of explanation is found in many textbooks across the sciences, particularly in biology, economics, and sociology. Contrary to this received wisdom, I argue that in many cases we are not justified in claiming that the central limit theorem explains why a particular quantity is normally distributed, and that in some (...) cases, we are actually wrong. 1 Introduction2 Normal Distributions and the Central Limit Theorem2.1 Normal distributions2.2 The central limit theorem2.3 Terminology3 Explaining Normality3.1 Loaves of bread3.2 Varying variances and probability densities3.3 Tensile strengths and problems with summation3.4 Products of factors and log-normal distributions3.5 Transforming factors and sub-factors3.6 Transformations of quantities3.7 Quantitative genetics3.8 Inference to the best explanation4 Maximum Entropy Explanations5 Conclusion. (shrink)

A central idea of developmental systems theory is ‘parity’ or ‘symmetry’ between genes and non-genetic factors of development. The precise content of this idea remains controversial, with different authors stressing different aspects and little explicit comparisons among the various interpretations. Here I characterise and assess several influential versions of parity.

This paper responds to Kenneth Waters’s account of actual difference making. Among other things, I argue that although Waters is right that researchers may sometimes be justified in focusing on genes rather than other causes of phenotypic traits, he is wrong that the apparatus of actual difference makers overcomes the traditional causal parity thesis.

Philosophers of science have developed an account of causal-mechanical explanation that captures regularity, but this account neglects variation. In this article I amend the philosophy of mechanisms to capture variation. The task is to explicate the relationship between regular causal mechanisms responsible for individual development and causes of variation responsible for variation in populations. As it turns out, disputes over this relationship have rested at the heart of the nature–nurture debate. Thus, an explication of the relationship between regular causal mechanisms (...) and causes of variation and between individual development and variation offers both the necessary amendment to the philosophy of mechanisms and the resources to mediate the dispute. (shrink)

Really statistical explanation is a hitherto neglected form of noncausal scientific explanation. Explanations in population biology that appeal to drift are RS explanations. An RS explanation supplies a kind of understanding that a causal explanation of the same result cannot supply. Roughly speaking, an RS explanation shows the result to be mere statistical fallout.