Quantitative genetics (QG) analyses variation in traits of humans, other animals, or plants in ways that take account of the genealogical relatedness of the individuals whose traits are observed. “Classical” QG, where the analysis of variation does not involve data on measurable genetic or environmental entities or factors, is reformulated in this article using models that are free of hypothetical, idealized versions of such factors, while still allowing for defined degrees of relatedness among kinds of individuals or “varieties.” The gene (...) - free formulation encompasses situations encountered in human QG as well as in agricultural QG. This formulation is used to describe three standard assumptions involved in classical QG and provide plausible alternatives. Several concerns about the partitioning of trait variation into components and its interpretation, most of which have a long history of debate, are discussed in light of the gene-free formulation and alternative assumptions. That discussion is at a theoretical level, not dependent on empirical data in any particular situation. Additional lines of work to put the gene-free formulation and alternative assumptions into practice and to assess their empirical consequences are noted, but lie beyond the scope of this article. The three standard QG assumptions examined are: (1) partitioning of trait variation into components requires models of hypothetical, idealized genes with simple Mendelian inheritance and direct contributions to the trait; (2) all other things being equal, similarity in traits for relatives is proportional to the fraction shared by the relatives of all the genes that vary in the population (e.g., fraternal or dizygotic twins share half of the variable genes that identical or monozygotic twins share); (3) in analyses of human data, genotype-environment interaction variance (in the classical QG sense) can be discounted. The concerns about the partitioning of trait variation discussed include: the distinction between traits and underlying measurable factors; the possible heterogeneity in factors underlying the development of a trait; the kinds of data needed to estimate key empirical parameters; and interpretations based on contributions of hypothetical genes; as well as, in human studies, the labeling of residual variance as a non-shared environmental effect; and the importance of estimating interaction variance. (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)

The book presents a new way of understanding Darwinism and evolution by natural selection, combining work in biology, philosophy, and other fields.

Woodward's long awaited book is an attempt to construct a comprehensive account of causation explanation that applies to a wide variety of causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defenses, objections, and replies into a convincing defense of the core of his theory, which is that we can analyze causation by appeal to the notion of manipulation.

Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.

Can a heritability value tell us something about the weight of genetic versus environmental causes that have acted in the development of a particular individual? Two possible questions arise. Q1: what portion of the phenotype of X is due to its genes and what portion to its environment? Q2: what portion of X’s phenotypic deviation from the mean is a result of its genetic deviation and what portion a result of its environmental deviation? An answer to Q1 provides the full (...) information about X’s development, while an answer to Q2 leaves out a large portion unexplained—that portion which corresponds to the phenotypic mean. Q1 is unanswerable, but I show it is nevertheless legitimate under certain quantitative genetics models. With regard to Q2, opinions in the philosophical and biological literature differ as to its legitimacy. I argue that not only is it legitimate, but in particular, under a few simplifying assumptions, it allows for a quantitative probabilistic answer: for normally distributed quantitative traits with no G-E correlation or statistical G × E interaction, we can assess the probability that X’s genes had a greater effect than its environment on its deviation from the mean population value. This probability is expressed as a function the heritability and the individual’s phenotypic value; we can also provide a quantitative probabilistic answer to Q2 for an arbitrary individual where the probability is a function only of heritability. (shrink)

The causal impacts of genes and environment on any one biological trait are inextricably entangled, and consequently it is widely accepted that it makes no sense in singleton cases to privilege either factor for particular credit. On the other hand, at a population level it may well be the case that one of the factors is responsible for more variation than the other. Standard methodological practice in biology uses the statistical technique of analysis of variance to measure this latter kind (...) of causal efficacy. In this paper, I argue that: 1) analysis of variance is in fact badly suited to this role; and. (shrink)

A high heritability estimate usually corresponds to a situation in which trait variation is largely caused by genetic variation. However, in some cases of gene-environment covariance, causal intuitions about the sources of trait difference can vary, leading experts to disagree as to how the heritability estimate should be interpreted. We argue that the source of contention for these cases is an inconsistency in the interpretation of the concepts ‘genotype’, ‘phenotype’, and ‘environment’. We propose an interpretation of these terms under which (...) trait variance initially caused by genetic variance is subsumed into a heritability for all cases of gene-environment covariance. (shrink)

Advocates of an ‘extended evolutionary synthesis’ have claimed that standard evolutionary theory fails to accommodate epigenetic inheritance. The opponents of the extended synthesis argue that the evidence for epigenetic inheritance causing adaptive evolution in nature is insufficient. We suggest that the ambiguity surrounding the conception of the gene represents a background semantic issue in the debate. Starting from Haig’s gene-selectionist framework and Griffiths and Neumann-Held’s notion of the evolutionary gene, we define senses of ‘gene’, ‘environment’, and ‘phenotype’ in a way (...) that makes them consistent with gene-centric evolutionary theory. We argue that the evolutionary gene, when being materialized, need not be restricted to nucleic acids but can encompass other heritable units such as epialleles. If the evolutionary gene is understood more broadly, and the notions of environment and phenotype are defined accordingly, current evolutionary theory does not require a major conceptual change in order to incorporate the mechanisms of epigenetic inheritance. _1_ Introduction _2_ The Gene-centric Evolutionary Theory and the ‘Evolutionary Gene’ _2.1_ The evolutionary gene _2.2_ Genes, phenotypes, and environments _3_ Epigenetic Inheritance and the Gene-Centred Framework _3.1_ Treating the gene as the sole heritable material? _3.2_ Epigenetics and phenotypic plasticity _4_ Conclusion. (shrink)

Gene–environment covariance is the phenomenon whereby genetic differences bias variation in developmental environment, and is particularly problematic for assigning genetic and environmental causation in a heritability analysis. The interpretation of these cases has differed amongst biologists and philosophers, leading some to reject the utility of heritability estimates altogether. This paper examines the factors that influence causal reasoning when G–E covariance is present, leading to interpretive disagreement between scholars. It argues that the causal intuitions elicited are influenced by concepts of agency (...) and blame-worthiness, and are intimately tied with the conceptual understanding of the phenotype under investigation. By considering a phenotype-specific approach, I provide an account as to why causal ascriptions can differ depending on the interpreter. Phenotypes like intelligence, which have been the primary focus of this debate, are more likely to spark disagreement for the interpretation of G–E covariance cases because the concept and ideas about its ‘normal development’ relatively ill-defined and are a subject of debate. I contend that philosophical disagreement about causal attributions in G–E covariance cases are in essence disagreements regarding how a phenotype should be defined and understood. This moves the debate from one of an ontological flavour concerning objective causal claims, to one concerning the conceptual, normative and semantic dependencies. (shrink)

The interventionist account of causation offers a criterion to distinguish causes from non-causes. It also aims at defining various desirable properties of causal relationships, such as specificity, proportionality and stability. Here we apply an information-theoretic approach to these properties. We show that the interventionist criterion of causation is formally equivalent to non-zero specificity, and that there are natural, information-theoretic ways to explicate the distinction between potential and actual causal influence. We explicate the idea that the description of causes should be (...) proportional to that of their effects. Then we draw a distinction between two ideas in the existing literature, the range of invariance of a causal relationship and its stability. The range of invariance is related to specificity and range of causal values. Stability concerns the effect of additional variables on the relationship between some focal pair of cause and effect variables. We show how to distinguish and measure the direct influence of background variables on the effect variable, and their influence on the relationship between the focal cause and the effect variable. Finally, we discuss the limitations of the information-theoretic approach, and offer prospects for complementary approaches. (shrink)

Heritability estimates obtained from genome-wide association studies are much lower than those of traditional quantitative methods. This phenomenon has been called the “missing heritability problem.” By analyzing and comparing GWAS and traditional quantitative methods, we first show that the estimates obtained from the latter involve some terms other than additive genetic variance, while the estimates from the former do not. Second, GWAS, when used to estimate heritability, do not take into account additive epigenetic factors transmitted across generations, while traditional quantitative (...) methods do. Given these two points we show that the missing heritability problem can largely be dissolved. (shrink)

Several authors have argued that causes differ in the degree to which they are ‘specific’ to their effects. Woodward has used this idea to enrich his influential interventionist theory of causal explanation. Here we propose a way to measure causal specificity using tools from information theory. We show that the specificity of a causal variable is not well-defined without a probability distribution over the states of that variable. We demonstrate the tractability and interest of our proposed measure by measuring the (...) specificity of coding DNA and other factors in a simple model of the production of mRNA. (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)

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)

With the advent of the Human Genome Project there have been many claims for the genetic origins of complex human behavior including insanity, criminality, and intelligence. But what does it really mean to call something 'genetic'? This is the fundamental question that Sahotra Sarkar's book addresses. The author analyses the nature of reductionism in classical and molecular genetics. He shows that there are two radically different kinds of reductionist explanation: genetic reduction (as found in classical genetics) and physical reduction (found (...) in molecular genetics). This important book clarifies the meaning of the term 'genetic', shows how molecular studies have affected genetics, and provides the philosophical background necessary to understand the debates over the Human Genome Project. It will be of particular interest to professionals and students in the philosophy of science, the history of science, and the social studies of science, medicine, and technology. (shrink)

The statistical technique of analysis of variance is often used by biologists as a measure of causal factors’ relative strength or importance. I argue that it is a tool ill suited to this purpose, on several grounds. I suggest a superior alternative, and outline some implications. I finish with a diagnosis of the source of error – an unwitting inheritance of bad philosophy that now requires the remedy of better philosophy.

Manipulablity theories of causation, according to which causes are to be regarded as handles or devices for manipulating effects, have considerable intuitive appeal and are popular among social scientists and statisticians. This article surveys several prominent versions of such theories advocated by philosophers, and the many difficulties they face. Philosophical statements of the manipulationist approach are generally reductionist in aspiration and assign a central role to human action. These contrast with recent discussions employing a broadly manipulationist framework for understanding causation, (...) such as those due to the computer scientist Judea Pearl and others, which are non-reductionist and rely instead on the notion of an intervention. This is simply an appropriately exogenous causal process; it has no essential connection with human action. This interventionist framework manages to avoid at least some of these difficulties faced by traditional philosophical versions of the manipulability theory and helps to clarify the content of causal claims. (shrink)

This paper describes an alternative to the common view that explanation in the special sciences involves subsumption under laws. According to this alternative, whether or not a generalization can be used to explain has to do with whether it is invariant rather than with whether it is lawful. A generalization is invariant if it is stable or robust in the sense that it would continue to hold under a relevant if it is stable or robust in the sense that it (...) would continue to hold under a relevant class of changes. Unlike lawfulness, invariance comes in degrees and has other features that are well suited to capture the characteristics of explanatory generalizations in the special sciences. For example, a generalization can be invariant even if it has exceptions or holds only over a limited spatio-temporal interval. The notion of invariance can be used to resolve a number of dilemmas that arise in standard treatments of explanatory generalizations in the special sciences. (shrink)

The interventionist account provides us with several notions permitting the qualification of causal relationships. In recent years, there has been a push toward formalizing these notions using information theory. In this paper, I discuss one of them, namely causal specificity. The notion of causal specificity is ambiguous as it can refer to at least two different concepts. After having presented these, I show that current attempts to formalize causal specificity in information theoretic terms have mostly focused on one of these (...) two concepts. I then propose and apply a new information-theoretic measure which captures the other concept. (shrink)

The interventionist account provides us with several notions permitting the qualification of causal relationships. In recent years, there has been a push toward formalizing these notions using information theory. In this paper, I discuss one of them, namely causal specificity. The notion of causal specificity is ambiguous as it can refer to at least two different concepts. After having presented these, I show that current attempts to formalize causal specificity in information theoretic terms have mostly focused on one of these (...) two concepts. I then propose and apply a new information-theoretic measure which captures the other concept. (shrink)

I argue that Calcott :481–505, Calcott 2017) mischaracterizes in an important way the notion of causal specificity proposed by Woodward :287–318, Woodward 2010). This leads him to rely too heavily on one single aspect of Woodward’s analysis on causal specificity; propose an information-theoretic measure he calls ‘precision’ which is partly redundant with, but less general than one of the dimensions in Woodward’s analysis of specificity, without acknowledging Woodward’s analysis; and claim that comparing the specificities of two or more causes under (...) what he calls a competitive analysis of causes, does not permit to capture the distinction between permissive and instructive causes. After having restated Woodward’s analysis of causal specificity, I present an information-theoretic measure which, although related to Calcott’s measure, is more general than his and corresponds to the notion of specificity he missed in Woodward's analysis. I then show how this measure can be used, together with mutual causal information, to distinguish permissive from instructive causes in a competitive analysis of causes. (shrink)

There are two ways evolution by natural selection is conceptualized in the literature. One provides a ‘recipe’ for ENS incorporating three ingredients: variation, differences in fitness, and heritability. The other provides formal equations of evolutionary change and partitions out selection from other causes of evolutionary changes such as transmission biases or drift. When comparing the two approaches there seems to be a tension around the concept of heritability. A recent claim has been made that the recipe approach is flawed and (...) should be abandoned. In this article, I show that the tension is only a superficial one. If one uses a concept of heritability strictly in line with the formal equations of evolutionary change, the recipe approach keeps its validity and generality. To demonstrate that the intuitive concept of heritability is not a general one, I use one formulation of the Price equation formulated by Okasha, show that the concept of heritability in his formulation incorporates both the intuitive notion of heritability as a measure of similarity between parent and offspring characters, and a measure of persistence. I advocate for persistence to be incorporated in the concept of heritability used in recipes for ENS in the same way heredity is, show that this is readily attainable and thereby dissolve any point of tension concerning heritability between the recipe and the analytical approach to ENS. 1 Introduction2 Heritability in the Price Equation2.1 Different approaches to heredity and heritability2.2 Heritability: From recipes to the Price equation3 A Problem for the Recipes?4 Reinterpreting Heritability for Recipes5 Conclusion Appendix. (shrink)

There is a tension between, on the one hand, the view that natural selection refers to individual-level causes, and on the other hand, the view that it refers to a population-level cause. In this article, I make the case for the individual-level cause view. I respond to recent claims made by McLoone that the individual-level cause view is inconsistent. I show that if one were to follow his arguments, any causal claim in any context would have to be regarded as (...) vindicating a form of population-level cause view. I show why this is implausible and how a consistent individual-level cause position can be held within the interventionist account of causation. Finally, I argue that there is one sense in which natural selection might be said to refer to population-level causes of evolutionary change. The upshot is that, as noted by others, natural selection can be regarded as referring to a population-level cause in the context of frequency-dependent selection and other situations of fitness-altering interactions between the individuals of a population. But whether this statement is true will depend on the empirical case investigated, not some a priori conceptual distinction. Thus, even though situations of frequency dependence might be ubiquitous, it is orthogonal to the conceptual question of whether frequency-independent natural selection—McLoone’s target—refers to individual- or population-level causes. (shrink)

I present a reconstruction of F.H.C. Crick's two 1957 hypotheses "Sequence Hypothesis" and "Central Dogma" in terms of a contemporary philosophical theory of causation. Analyzing in particular the experimental evidence that Crick cited, I argue that these hypotheses can be understood as claims about the actual difference-making cause in protein synthesis. As these hypotheses are only true if restricted to certain nucleic acids in certain organisms, I then examine the concept of causal specificity and its potential to counter claims about (...) causal parity of DNA and other cellular components. I first show that causal specificity is a special kind of invariance under interventions, namely invariance of generalizations that range over finite sets of discrete variables. Then, I show that this notion allows the articulation of a middle ground in the debate over causal parity. (shrink)

Is the history of life a series of accidents or a drama scripted by selfish genes? Is there an “essential” human nature, determined at birth or in a distant evolutionary past? What should we conserve—species, ecosystems, or something else? -/- Informed answers to questions like these, critical to our understanding of ourselves and the world around us, require both a knowledge of biology and a philosophical framework within which to make sense of its findings. In this accessible introduction to philosophy (...) of biology, Kim Sterelny and Paul E. Griffiths present both the science and the philosophical context necessary for a critical understanding of the most exciting debates shaping biology today. The authors, both of whom have published extensively in this field, describe the range of competing views—including their own—on these fascinating topics. -/- With its clear explanations of both biological and philosophical concepts, Sex and Death will appeal not only to undergraduates, but also to the many general readers eager to think critically about the science of life. (shrink)

The method in human genetics of ascribing causal responsibility to genotype by the use of heritability estimates has been heavily criticized over the years. It has been argued that these estimates are rarely valid and do not serve the purpose of tracing genetic causes. Recent contributions strike back at this criticism. I present and discuss two opposing views on these matters represented by Richard Lewontin and Neven Sesardic, and I suggest that some of the disagreement is based on differing concepts (...) of genetic causation. I use the distinction of structuring and triggering causes to help clarifying the basis for the opposing views. (shrink)

This article examines eight “gaps” in order to clarify why the quantitative genetics methods of partitioning variation of a trait into heritability and other components has very limited power to show anything clear and useful about genetic and environmental influences, especially for human behaviors and other traits. The first two gaps should be kept open; the others should be bridged or the difficulty of doing so should be acknowledged: 1. Key terms have multiple meanings that are distinct; 2. Statistical patterns (...) are distinct from measurable underlying factors; 3. Translation from statistical analyses to hypotheses about measurable factors is difficult; 4. Predictions based on extrapolations from existing patterns of variation may not match outcomes; 5. The partitioning of variation in human studies does not reliably estimate the intended quantities; 6. Translation from statistical analyses to hypotheses about the measurable factors is even more difficult in light of the possible heterogeneity of underlying genetic or environmental factors; 7. Many steps lie between the analysis of observed traits and interventions based on well-founded claims about the causal influence of genetic or environmental factors; 8. Explanation of variation within groups does not translate to explanation of differences among groups. At the start, I engage readers’ attention with three puzzles that have not been resolved by past debates. The puzzles concern generational increases in IQ test scores, the possibility of underlying heterogeneity, and the translation of methods from selective breeding into human genetics. After discussing the gaps, I present each puzzle in a new light and point to several new puzzles that invite attention from analysts of variation in quantitative genetics and in social science more generally. The article’s critical perspectives on agricultural, laboratory, and human heritability studies are intended to elicit further contributions from readers across the fields of history, philosophy, sociology, and politics of biology and in the sciences. (shrink)

The presence of gene–environment statistical interaction and correlation in biological development has led both practitioners and philosophers of science to question the legitimacy of heritability estimates. The paper offers a novel approach to assess the impact of GxE and rGE on the way genetic and environmental causation can be partitioned. A probabilistic framework is developed, based on a quantitative genetic model that incorporates GxE and rGE, offering a rigorous way of interpreting heritability estimates. Specifically, given an estimate of heritability and (...) the variance components associated with estimates of GxE and rGE, I arrive at a probabilistic account of the relative effect of genes and environment. (shrink)