David Teira

Experimental balance is usually understood as the control for the value of the conditions, other than the one under study, which are liable to affect the result of a test. We discuss three different approaches to balance. ‘Millean balance’ requires identifying and equalizing ex ante the value of these conditions in order to conduct solid causal inferences. ‘Fisherian balance’ measures ex post the influence of uncontrolled conditions through the analysis of variance. In ‘efficiency balance’ the value of the antecedent conditions is decided ex ante according to the efficiency they yield in the estimation of the treatment outcome. Against some old arguments by Worrall, we show that in both Fisherian and efficiency balance there are good reasons to randomize the allocation of treatments, in particular when there is no agreement among experimenters as to the antecedent conditions to be controlled for.

Fact-checking agencies assess and score the truthfulness of politicians’ claims to foster their electoral accountability. Fact-checking is sometimes presented as a quasi-scientific activity, based on reproducible verification protocols that would guarantee an unbiased assessment. We will study these verification protocols and discuss under which conditions fact-checking could achieve effective reproducibility. Through an analysis of the methodological norms in verification protocols, we will argue that achieving reproducible fact-checking may not help much in rendering politicians accountable. Political fact-checkers do not deliver either reproducibility or accountability today, and there are reasons to think that traditional quality journalism may serve liberal democracies better.

Publishers are signing transformative agreements with different research institutions and funding bodies across the world. These agreements establish that the institution or funder makes a block payment in exchange for an annual quota of OA papers allowing their affiliated authors to publish OA in an agreed list of journals, at no extra cost to the individual author. This is a step towards the transformation of these journals into a Gold (commercial) Open Access regime. In January 2023, the members of the OA working group contacted the editors of the 27 journals listed as relevant for the field of philosophy of science on the website of the Philosophy of Science Association. We asked the editors-in-chief two questions: 1) Did your publishers discuss any plans to transition to gold open access exclusively with you? If so, how would it work? 2) If the publishers did contact you regarding this matter, what is your view on the project? We collected 22 answers. The executive summary is: 10 of the 27 journals have started the transition to Gold Open Access, although many editors are not aware of this shift, but they wouldn’t support it, and many are actively against it.

We defend, from a contractarian perspective, that the fair price of an insurance policy is the amount that the contracting parties agree when they are both equally uncertain about the insured event. Drawing on the approach developed by R. Sugden in _The Community of Advantage_, we answer two standard objections raised against contractarianism in the actuarial sciences: (1) people are not wise enough to assess their actuarial risks; (2) they are not rational enough to decide which insurance policy suits them better. We show under which circumstances people can make fair actuarial agreements, without presupposing any objective risk or rationality benchmarks.

Political fact-checkers evaluate the truthfulness of politicians’ claims. This paper contributes to an emerging scholarly debate on whether fact-checkers treat political parties differently in a systematic manner depending on their ideology (bias). We first examine the available approaches to analyze bias and then present a new approach in two steps. First, we propose a logistic regression model to analyze the outcomes of fact-checks and calculate how likely each political party will obtain a truth score. We test our model with a sample of fact-checks from Newtral, a major Spanish fact-checker. Our model would signal bias under two assumptions: a) all political parties are on average equally accurate in their statements; b) the verification method gives precise instructions and is implemented systematically. We investigate this second assumption with a series of interviews with Newtral fact-checkers. We show that standard verification protocols are so loosely implemented that fact-checks reflect a set of journalistic decisions, rather than a bias in the statistical sense. We call for a more rigorous definition of verification methods as a pre-requisite for an unbiased assessment of politician’s claims.

Experimental balance is usually understood as the control for the value of the conditions, other than the one under study, which are liable to affect the result of a test. We will discuss three different approaches to balance. ‘Millean balance’ requires to identify and equalize ex ante the value of these conditions in order to conduct solid causal inferences. ‘Fisherian balance’ measures ex post the influence of uncontrolled conditions through the analysis of variance. In ‘efficiency balance’ the value of the antecedent conditions is decided ex ante according to the efficiency they yield in the estimation of the treatment outcome. Against some old arguments by John Worrall, we will show that in both Fisherian and efficiency balance there are good reasons to randomize the allocation of treatments, in particular when there is no agreement among experimenters as to the antecedent conditions to be controlled for.

This article analyses how the French Academy of Sciences assessed Jaime Ferrán’s cholera vaccine submitted for the Prix Bréant in the 1880s. Ferrán, a Spanish independent physician, discovered the treatment in 1884 and tried it on thousands of patients during the cholera outbreak in Valencia the following year. His evaluation sparked a controversy in Spain and abroad on the vaccine’s efficacy. The Bréant jury did not see any evidence for it in Ferrán’s submission, a decision usually interpreted in terms of French scientific nationalism (or simple chauvinism): an outsider from the scientific periphery could not be awarded the Bréant. Drawing on the archival records of the award, we suggest that Ferrán failed instead to provide data that the Academy could consider unbiased, according to the contemporary standards for data presentation. We will illustrate these standards at work in the assessment of another submission from Spain, by Philipp Hauser, who received the Bréant for the thoroughness of his statistical endeavour

Breeds are classifications of domestic animals that share, to a certain degree, a set of conventional phenotypic traits. We are going to defend that, despite classifying biological entities, animal breeds are social kinds. We will adopt Godman’s view of social kinds, classifications with predictive power based on social learning processes. We will show that, although the folk concept of animal breed refers to a biological kind, there is no way to define it. The expert definitions of breeds are instead based on socially learnt conventions and skills (artificial selection), yielding groupings in which scientific predictions are possible. We will discuss in what sense breeds are social, but not human kinds and in what sense the concept of a breed is necessary to make them real.

Statistical evidence is pervasive in medicine. In this chapter we will focus on the reliability of randomized clinical trials (RCTs) conducted to test the safety and efficacy of medical treatments. RCTs are scientific experiments and, as such, we expect them to be replicable: if we repeat the same experiment time and again, we should obtain the same outcome (Norton 2015). The statistical design of the test should guarantee that the observed outcome is not a random event, but rather a real effect of the treatments administered. However, for more than a decade now, we have been discussing a replicability crisis across different experimental disciplines including medicine: the outcomes of trials published in very prestigious journals often disappear when the experiment is repeated (see, e.g., Lehrer 2010, Begley and Ellis 2012, Horton 2015). There are different accounts of the reason for this replicability crisis, ranging from scientific fraud to lack of institutional incentives to double-check someone else’s results. In this chapter we will use the replicability crisis as a thread to introduce some central issues in the design of scientific experiments in medicine. First, in section 1, we will see how replicability and statistical significance are connected: we can only make sense of the p-value of a trial outcome within a series of replications of the test. But in order to conduct these replications properly, we need to agree on the proper design of the experiment we are going to repeat. In particular, we need to prevent the preferences of the experimenters from biasing the outcome of the experiment. If there is such a bias, when the experiment is replicated by a third party, the observed outcome will vanish. In section 2, we will argue that trialists need to agree on the debiasing procedures and the statistical quality controls that feature in the trial protocol, if they want the outcome to be replicable. In section 3, we will make two complementary points. On the one hand, replicability per se is not everything: we need trial outcomes that are not only statistically significant but also clinically relevant. On the other hand, trials are not everything: the experts analyzing the evidence can improve the reliability of statistical evidence, although they sometimes fail; we need to further study how they make their decisions. In section 4, we will use a controversy about the overprescription of statins to show how non-replicable effects are obtained in trials and how experts may fail at detecting such flaws, if the commercial interests are big enough.

Randomized controlled trials test new drugs using various debiasing devices to prevent participants from manipulating the trials. But participants often dislike controls, arguing that they impose a paternalist constraint on their legitimate preferences. The 21st Century Cures Act, passed by US Congress in 2016, encourages the Food and Drug Administration to use alternative testing methods, incorporating participants’ preferences, for regulatory purposes. We discuss, from a historical perspective, the trade-off between trial impartiality and participants’ freedom. We argue that the only way out is considering which methods improve upon the performance of conventional trials in keeping dangerous or inefficacious compounds out of pharmaceutical markets.

This paper discusses the so-called non-interference assumption (NIA) grounding causal inference in trials in both medicine and the social sciences. It states that for each participant in the experiment, the value of the potential outcome depends only upon whether she or he gets the treatment. Drawing on methodological discussion in clinical trials and laboratory experiments in economics, I defend the necessity of partial forms of blinding as a warrant of the NIA, to control the participants’ expectations and their strategic interactions with the experimenter

Over the last decade, philosophers of science have extensively criticized the epistemic superiority of randomized controlled trials for testing safety and effectiveness of new drugs, defending instead various forms of evidential pluralism. We argue that scientific methods in regulatory decision-making cannot be assessed in epistemic terms only: there are costs involved. Drawing on the legal distinction between rules and standards, we show that drug regulation based on evidential pluralism has much higher costs than our current RCT-based system. We analyze these costs and advocate for evaluating any scheme for drug regulatory tests in terms of concrete empirical benchmarks, like the error rates of regulatory decisions.

Our goal in this paper is to articulate a precise concept of at least a certain kind of disease-mongering, showing how pharmaceutical marketing can commercially exploit certain diseases when their best definition is given through the success of a treatment in a clinical trial. We distinguish two types of disease-mongering according to the way they exploit the definition of the trial population for marketing purposes. We argue that behind these two forms of disease-mongering there are two well-known problems in the statistical methodology of clinical trials (the reference class problem and the distinction between statistical and clinical significance). Overcoming them is far from simple

I analyze cultural relativism as a methodological strategy to correct for ethnocentric biases in anthropological fieldwork. I discuss the format debiasing norms may adopt depending on whether a discipline has a causal or interpretative outlook. Franz Boas and his school advocated for an interpretative approach to ethnographic fieldwork, in which cultural relativism was implemented as a standard to be interpreted by expert third parties. Legitimate as it may be as a debiasing method, it does not allow anthropologists to adjudicate their debates on biases in their ethnographic record.

One of the defining features of the classical gene was its position. In molecular genetics, positions are defined instead as nucleotide numbers and there is no clear correspondence with its classical counterpart. However, the classical gene position did not simply disappear with the development of the molecular approach, but survived in the lab associated to different genetic practices. The survival of classical gene position would illustrate Waters’ view about the practical persistence of the genetic approach beyond reductionism and anti-reductionist claims. We show instead that at the level of laboratory practices there are also reductive processes, operating through the rise and fall of different techniques. Molecular markers made the concept of classical gene position practically dispensable, leading us to rethink whether it had any causal role or was just a mere heuristic.

Pharmaceutical paternalism is the normative stance upheld by pharmaceutical regulatory agencies like the US Food and Drug Administration. These agencies prevent patients from accessing treatments declared safe and ineffective for the patient’s good without their consent. Libertarian critics of the FDA have shown a number of significant flaws in regulatory paternalism. Against these objections, I will argue that, in order to make an informed decision about treatments, a libertarian patient should request full disclosure of the uncertainty about an experimental treatment. But pharmaceutical markets, on their own, are not a reliable source of information about such uncertainty. And companies have the power to capture any independent expert who may assess it. Therefore, the libertarian is better off deferring on an independent regulatory body the assessment of the pharmaceutical risks, constraining access to treatments until tested.

In actuarial parlance, the price of an insurance policy is considered fair if customers bearing the same risk are charged the same price. The estimate of this fair amount hinges on the expected value obtained by weighting the different claims by their probability. We argue that, historically, this concept of actuarial fairness originates in an Aristotelian principle of justice in exchange (equality in risk). We will examine how this principle was formalized in the 16th century and shaped in life insurance during the following two hundred years, in two different interpretations. The Domatian account of actuarial fairness relied on subjective uncertainty: An agreement on risk was fair if both parties were equally ignorant about the chances of an uncertain event. The objectivist version grounded any agreement on an objective risk estimate drawn from a mortality table. We will show how the objectivist approach collapsed in the market for life annuities during the 18th century, leaving open the question of why we still speak of actuarial fairness as if it were an objective expected value.

In this paper, Isuggest that placebo effects, as we know them today, should be understood as experimental phenomena, low-level regularities whose causal structure is grasped through particular experimental designs with little theoretical guidance. Focusing on placebo interventions with needles for pain reduction -one of the few placebo regularities that seems to arise in meta-analytical studies- I discuss the extent to which it is possible to decompose the different factors at play through more fine-grained randomized clinical trials. My sceptical argument is twofold. On the one hand, I argue that experiments alone are not enough to standardize interventions, and that it is necessary to include theories. On the other hand, I argue that the social interactions that seem to be part of placebo effects are difficult, if not impossible, to blind. Therefore, the measurement biases arising from the participants’ reactivity to the experimental setup cannot be controlled for. Further decomposition of placebo effects requires a theoretical account of the existing experimental regularities that may guide further tests

I want to expand the pragmatist view of probability advocated by Roberto Torretti, drawing on the propensity approach. In the first part, I will show how the concept of mathematical expectation originally formalized an Aristotelian principle of justice. In the second half, I will present the game-theoretic approach to probability developed by Shafer and Vovk, showing how it allows us to interpret the original normativity of mathematical expectations. I will finally discuss how this latter view contributes to a pragmatist understanding of probability, as Torretti advocates, although with broader foundations than the physical symmetries of standard propensities.

Exploramos aquí la conexión entre los conceptos de riesgo e igualdad en el argumento del observador imparcial. La concepción de la justicia que elegiría un observador imparcial se justifica por la pureza del procedimiento de elección. Sin embargo, si modelizamos esta decisión utilizando medidas del riesgo habituales en matemática financiera, veremos cómo el criterio de elección del observador bajo el velo de la ignorancia contiene una preferencia implícita por el grado de desigualdad resultante. Esto nos obliga a reconsiderar la pureza procedimental de la elección.