Causal Assessment of Biological Impairment in the Little Floyd River,Iowa, USA

An assessment of biological impairment in the Little Floyd River (Iowa, USA) was based on evidence of three characteristics of causation: co-occurrence, preceding causation, and sufficiency. Evidence of the physical interaction of the probable causes and the biota, resulting alterations to the biota, as well as the time order of the cause and the effect were consistent within the assessment, but the evidence for these causal characteristics did not discriminate among probable causes or other causes. Deposited sediment, low dissolved oxygen, heat stress, and ammonia toxicity are the probable causes of impaired biological condition in the Little Floyd River compared with other rivers in the ecoregion. Less likely causes are suspended sediment, altered basal food resources, and flow alteration. Very unlikely causes are pH shifts, total dissolved solids, Cyprinus carpio (an invasive species), metal toxicity, and pesticides. Data were insufficient to assess salinity or other toxicants. The assessment was used to develop a recovery plan for the stream. This assessment demonstrates that, even when there are many candidate causes and uncertainties are substantial, the probable causes of biological impairments can be determined with enough certainty to inform decision-making to address environmental problems.     

Assessment of Probable Causes of Reduced Aquatic Life in the Touchet River,Washington, USA

The Touchet River in eastern Washington State is the site of the first causal assessment in the arid Northwest for salmonids using the U.S. Environmental Protection Agency's (USEPA's) Stressor Identification process. Seven candidate causes that affect salmonid density and macroinvertebrate abundance and diversity were considered: toxics, warm water temperature, sedimentation, low dissolved oxygen, alkaline pH, reduced detritus, and reduced habitat complexity. Candidate causes were evaluated using several types of evidence of preceding causation, co-occurrence, sufficiency, and alteration along with evaluation of the consistency of that evidence and consistency with other assessments. Evidence was scored, and the body of evidence was weighed based on credibility, strength, diversity, and coherence. Warm water temperature and sedimentation were highly probable causes of altered biological condition. Low dissolved oxygen and alkaline pH were also a problem for some areas but were less severe than temperature and sediment. Water removal and reduced habitat complexity and canopy cover were not directly causal but could affect sedimentation and temperature. This case study is noteworthy for using assemblage symptomology associated with temperature, sediment, and detritus as a type of evidence and for physiographically matching reference sites for comparisons and evaluation of natural and cumulative anthropogenic stressor gradients in the absence of state biological criteria.     

The Science and Philosophy of a Method for Assessing Environmental Causes

When an environmental impairment has been identified, it becomes necessary to identify the cause so that an appropriate action can be planned. However, causation is difficult to establish—both conceptually and in practice. To ensure that the U.S. Environmental Protection Agency's (USEPA's) method for causal assessment is appropriate and defensible, we reviewed concepts of causation from philosophers, statisticians, epidemiologists, and others. This article summarizes the results of that review and explains how it relates to the USEPA's method. We include a five-step process: (1) identify alternative candidate causes; (2) logically eliminate when possible; (3) diagnose when possible; (4) analyze the strength of evidence for remaining candidate causes; and (5) identify the most likely cause. We also encourage three practices: (1) use a consistent process; (2) do not claim proof of causation; and (3) document the evidence and inferences. This approach allows assessors to identify the most likely cause or, failing that, to reduce the set of possible causes and identify information needs for another iteration of causal assessment.     

Methodologic Implications of the Precautionary Principle: Causal Criteria

Applying the Precautionary Principle to public health requires a re-evaluation of the methods of inference currently used to make claims about disease causation from epidemiologic and other forms of scientific evidence. In current thinking, a well-established, near-certain causal relationship implies highly consistent statistically significant results across many different studies, large relative risk estimates, extensive understanding of biological mechanisms and dose-response relationships, positive prevention trial results, a clear temporal relationship between cause and effect, and other conditions spelled out in terms of the widely-used causal criteria. The Precautionary Principle, however, states that preventive measures are to be taken when cause and effect relationships are not fully established scientifically. What evidentiary conditions, as reflected in the causal criteria, will be certain enough to warrant precautionary preventive action? This paper argues that minimum evidentiary requirements for causation need to be articulated if the Precautionary Principle is to be successfully incorporated into public health practice. Two precautionary changes to criteria-based methods of causal inference are examined: reducing the number of criteria and weakening the rules of inference accompanying the criteria. Such changes point in the direction of identifying minimum evidentiary conditions, but would be premature without better understanding how well current methods of causal inference work.     

Extraction of Pharmacokinetic Evidence of Drug–Drug Interactions from the Literature

Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F1≈0.93, MCC≈0.74, iAUC≈0.99) and sentences (F1≈0.76, MCC≈0.65, iAUC≈0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.     

Causal structure and hierarchies of models

Economics prefers complete explanations: general over partial equilibrium, microfoundational over aggregate. Similarly, probabilistic accounts of causation frequently prefer greater detail to less as in typical resolutions of Simpson’s paradox. Strategies of causal refinement equally aim to distinguish direct from indirect causes. Yet, there are countervailing practices in economics. Representative-agent models aim to capture economic motivation but not to reduce the level of aggregation. Small structural vector-autoregression and dynamic stochastic general-equilibrium models are practically preferred to larger ones. The distinction between exogenous and endogenous variables suggests partitioning the world into distinct subsystems. The tension in these practices is addressed within a structural account of causation inspired by the work of Herbert Simon’s, which defines cause with reference to complete systems adapted to deal with incomplete systems and piecemeal evidence. The focus is on understanding the constraints that a structural account of causation places on the freedom to model complex or lower-order systems as simpler or higher-order systems and on to what degree piecemeal evidence can be incorporated into a structural account.     

The Central Dogma as a thesis of causal specificity

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.     

A realist approach to biological events

In this paper, I aim to show that the multiple realisability and the causal efficacy of biological events can best be explained by construing biological events as determinables of more determinate physical events. The determination relation itself is spelled out in terms of inclusive essence. In order to secure actual causation for biological events (in contrast to causal influence), two conditions are introduced such that for some events, biological events qualify as their cause. Finally, certain consequences of the presented theory are discussed, such as the question of how the biological token event can retain its identity across modal modifications of its realiser, and such as how the presented solution bears on the classical problem of biological causation.     

Using Field-Based Species Sensitivity Distributions to Infer Multiple Causes

We describe a new way to develop evidence of causes of biological effects using field-based species sensitivity distributions (SSDs) and show how evidence can be compared when genera or effect endpoints are different among potentially causal agents. To evaluate if a cause is sufficient to elicit an effect, we developed a general SSD. A cause was judged sufficient if the intensity of the stressor at the site predicted the observed proportion of extirpation. To evaluate if an effect is specific to a cause, we developed site-specific SSDs using field-based effect levels of genera occurring in the locality of the study. An effect was judged specific to a cause if susceptible genera were absent and tolerant genera were present. Field-based SSDs were used to assess nutrients and conductivity. Other associations were used to assess metals, sediment, dissolved oxygen, and temperature. A case study at Pigeon Roost Creek, Tennessee, USA, illustrates how the SSDs are used to infer multiple causes. A weight-of-evidence analysis identified nutrients and sediment as probable causes but another unidentified agent appears to be acting as well. This inferential approach has broad application and the causal models for conductivity, nutrients, and deposited sediment can be used at other locations.     

Mechanisms, determination and the metaphysics of neuroscience

In this paper, I evaluate recently defended mechanistic accounts of the unity of neuroscience from a metaphysical point of view. Considering the mechanistic framework in general (Sections 2 and 3), I argue that explanations of this kind are essentially reductive (Section 4). The reductive character of mechanistic explanations provides a sufficiency criterion, according to which the mechanism underlying a certain phenomenon is sufficient for the latter. Thus, the concept of supervenience can be used in order to describe the relation between mechanisms and phenomena (Section 5). Against this background, I show that the mechanistic framework is subject to the causal exclusion problem and faces the classical metaphysical options when it comes to the relations obtaining between different levels of mechanisms (Section 6). Finally, an attempt to improve the metaphysics of mechanisms is made (Section 7) and further difficulties are pointed out (Section 8).     

Causal reasoning,causal probabilities,and conceptions of causation

The present paper deals with the tools that can be used to represent causation and to reason about it and, specifically, with their diversity. It focuses on so-called “causal probabilities”—that is, probabilities of effects given one of their causes—and critically surveys a recent paper in which Joyce (2010) argues that the values of these probabilities do not depend on one’s conception of causation. I first establish a stronger independence claim: I show that the very definition of causal probabilities is independent of one’s conception of causation. Second, I investigate whether causal probabilities indeed take the same values under their different possible definitions.     

Disentangling interactions between adaptive divergence and gene flow when ecology drives diversification

Adaptive diversification is driven by selection in ecologically different environments. In absence of geographical barriers to dispersal, this adaptive divergence (AD) may be constrained by gene flow (GF). And yet the reverse may also be true, with AD constraining GF (i.e. 'ecological speciation'). Both of these causal effects have frequently been inferred from the presence of negative correlations between AD and GF in nature - yet the bi-directional causality warrants caution in such inferences. We discuss how the ability of correlative studies to infer causation might be improved through the simultaneous measurement of multiple ecological and evolutionary variables. On the one hand, inferences about the causal role of GF can be made by examining correlations between AD and the potential for dispersal. On the other hand, inferences about the causal role of AD can be made by examining correlations between GF and environmental differences. Experimental manipulations of dispersal and environmental differences are a particularly promising approach for inferring causation. At present, the best studies find strong evidence that GF constrains AD and some studies also find the reverse. Improvements in empirical approaches promise to eventually allow general inferences about the relative strength of different causal interactions during adaptive diversification.     

The Causal Analysis Framework: Refining Approaches and Expanding Multidisciplinary Applications

This series of articles provides perspectives and recent case examples reflective of the growing interest in and need for formal causal analysis procedures that narrow the uncertainties associated with understanding cause and effect relationships in environmental and health matters. That understanding is important for guiding prevention, remediation, and/or restoration efforts pertaining to environmental stressors. A defensible establishment or refutation of causes is often needed to support legal opinions and/or reach decisions on specific regulatory actions. For complex environmental and health matters, the ability to support cause and effect relationships to a reasonable degree of certainty depends not only on the existence of the relationships but also on the analyst's ability to examine alternative possibilities and to use available evidence to support scientific opinion. Formal casual analyses have evolved to provide analysts with organized frameworks for weighing evidence and decreasing the likelihood of missing important aspects of cause and effect relationships as problems become increasingly complex and less familiar. In this perspectives series, the causal analysis method is explored through additional examination of the underlying philosophies and history of approaches that serve as the foundation of what we consider causal analysis today. In addition, examples of applied causal analysis provide insights into the challenges and benefits of a well thought out causal analysis.     

Teaching critical thinking in a developmental biology course at an American liberal arts college

We all expect our students to learn facts and concepts, but more importantly, we want them to learn how to evaluate new information from an educated and skeptical perspective; that is, we want them to become critical thinkers. For many of us who are scientists and teachers, critical thought is either intuitive or we learned it so long ago that it is not at all obvious how to pass on the skills to our students. Explicitly discussing the logic that underlies the experimental basis of developmental biology is an easy and very successful way to teach critical thinking skills. Here, I describe some simple changes to a lecture course that turn the practice of critical thinking into the centerpiece of the learning process. My starting point is the "Evidence and Antibodies" sidelight in Gilbert's Developmental Biology (2000), which I use as an introduction to the ideas of correlation, necessity and sufficiency, and to the kinds of experiments required to gather each type of evidence: observation ("show it"), loss of function ("block it") and gain of function ("move it"). Thereafter, every experiment can be understood quickly by the class and discussed intelligently with a common vocabulary. Both verbal and written reinforcement of these ideas dramatically improve the students' ability to evaluate new information. In particular, they are able to evaluate claims about cause and effect; they become experts at distinguishing between correlation and causation. Because the intellectual techniques are so powerful and the logic so satisfying, the students come to view the critical assessment of knowledge as a fun puzzle and the rigorous thinking behind formulating a question as an exciting challenge.     

Making sense of downward causation in manipulationism: illustrations from cancer research

Many researchers consider cancer to have molecular causes, namely mutated genes that result in abnormal cell proliferation (e.g. Weinberg 1998). For others, the causes of cancer are to be found not at the molecular level but at the tissue level where carcinogenesis consists of disrupted tissue organization with downward causation effects on cells and cellular components (e.g. Sonnenschein and Soto 2008). In this contribution, I ponder how to make sense of such downward causation claims. Adopting a manipulationist account of causation (Woodward 2003), I propose a formal definition of downward causation and discuss further requirements (in light of Baumgartner 2009). I then show that such an account cannot be mobilized in support of non-reductive physicalism (contrary to Raatikainen 2010). However, I also argue that such downward causation claims might point at particularly interesting dynamic properties of causal relationships that might prove salient in characterizing causal relationships (following Woodward 2010).     

Getting rid of interventions

According to James Woodward’s influential interventionist account of causation, X is a cause of Y iff, roughly, there is a possible intervention on X that changes Y. Woodward requires that interventions be merely logically possible. I will argue for two claims against this modal character of interventions: First, merely logically possible interventions are dispensable for the semantic project of providing an account of the meaning of causal statements. If interventions are indeed dispensable, the interventionist theory collapses into (some sort of) a counterfactual theory of causation. Thus, the interventionist theory is not tenable as a theory of causation in its own right. Second, if one maintains that merely logically possible interventions are indispensable, then interventions with this modal character lead to the fatal result that interventionist counterfactuals are evaluated inadequately. Consequently, interventionists offer an inadequate theory of causation. I suggest that if we are concerned with explicating causal concepts and stating the truth-conditions of causal claims we best get rid of Woodwardian interventions.     

Assessing Causal Mechanistic Interactions: A Peril Ratio Index of Synergy Based on Multiplicativity

The assessments of interactions in epidemiology have traditionally been based on risk-ratio, odds-ratio or rate-ratio multiplicativity. However, many epidemiologists fail to recognize that this is mainly for statistical conveniences and often will misinterpret a statistically significant interaction as a genuine mechanistic interaction. The author adopts an alternative metric system for risk, the ‘peril’. A peril is an exponentiated cumulative rate, or simply, the inverse of a survival (risk complement) or one plus an odds. The author proposes a new index based on multiplicativity of peril ratios, the ‘peril ratio index of synergy based on multiplicativity’ (PRISM). Under the assumption of no redundancy, PRISM can be used to assess synergisms in sufficient cause sense, i.e., causal co-actions or causal mechanistic interactions. It has a less stringent threshold to detect a synergy as compared to a previous index of ‘relative excess risk due to interaction’. Using the new PRISM criterion, many situations in which there is not evidence of interaction judged by the traditional indices are in fact corresponding to bona fide positive or negative synergisms.     

Making Causal Claims about Environmentally Induced Adverse Effects

Causal inferences are a vital and intrinsic part of assessing the risk of adverse effects on human populations and ecological resources from biological, chemical, physical, and psychosocial stressors. While it is well known that a statistical association does not necessarily imply a causal association, the central role of causal theory in health and ecological risk assessment is often overlooked. In this article, we present a succinct account of causal theory in the health sciences, emphasize the importance of differentiating between formal and informal approaches to causal inference, describe the weight-of-evidence process that is currently the predominant means of inferring causality in the context of science-based regulatory decisions, and discuss the effects of causal theory on the current and future practice of risk assessment. Our aim is to highlight the significance of decisions about causation and causal inference, and to suggest that explicit, well-considered choices will serve to strengthen the scientific underpinnings of regulatory decision-making.     

Living with observational data in biological anthropology

The establishment of cause and effect relationships is a fundamental objective of scientific research. Many lines of evidence can be used to make cause–effect inferences. When statistical data are involved, alternative explanations for the statistical relationship need to be ruled out. These include chance (apparent patterns due to random factors), confounding effects (a relationship between two variables because they are each associated with an unmeasured third variable), and sampling bias (effects due to preexisting properties of compared groups). The gold standard for managing these issues is a controlled randomized experiment. In disciplines such as biological anthropology, where controlled experiments are not possible for many research questions, causal inferences are made from observational data. Methods that statisticians recommend for this difficult objective have not been widely adopted in the biological anthropology literature. Issues involved in using statistics to make valid causal inferences from observational data are discussed.