How necessary are randomized controlled trials?

Randomized controlled trials (RCTs) are often deemed the gold standard for testing new treatments. This belief in turn justifies recruiting patients into such trials even when it is suspected that a new treatment is superior – although patients in the control group are thereby denied what might be the better treatment, we cannot know that the treatment actually is better, the thought runs, without conducting an RCT. But Robert Northcott argues that RCTs are not always the best choice after all. Rather, like any other method, they can go wrong sometimes, in several different ways. The main alternative to them is historical studies, which try to assess a treatment’s effectiveness from data not drawn from trials. These too can go wrong in several ways, and in the past have acquired a bad reputation. However, that prejudice has become outdated. The truer picture, Northcott argues, is that sometimes one method is preferable, sometimes the other. Things must be decided case by case. It follows that the ethical ramifications of conducting an RCT also must be examined case by case; there is no one-size-fits-all answer. An especially striking and emotional example concerns ECMO, a treatment for newborn babies with life-threatening lung problems. Historical studies indicated that ECMO was a major breakthrough, offering hugely increased survival rates. But it was still insisted that it also be tested in RCTs, in the course of which many babies receiving the conventional treatment died. A properly nuanced view of RCTs suggests that these deaths were tragically unnecessary

Opinion polling and election predictions

Election prediction by means of opinion polling is a rare empirical success story for social science, but one not previously considered by philosophers. I examine the details of a prominent case, namely the 2012 US presidential election, and draw two lessons of more general interest: 1) Methodology over metaphysics. Traditional metaphysical criteria were not a useful guide to whether successful prediction would be possible; instead, the crucial thing was selecting an effective methodology. 2) Which methodology? Success required sophisticated use of case-specific evidence from opinion polling. The pursuit of explanations via general theory or causal mechanisms, by contrast, turned out to be precisely the wrong path – contrary to much recent philosophy of social science

Conceived this way: innateness defended

We propose a novel account of the distinction between innate and acquired biological traits: biological traits are innate to the degree that they are caused by factors intrinsic to the organism at the time of its origin; they are acquired to the degree that they are caused by factors extrinsic to the organism. This account borrows from recent work on causation in order to make rigorous the notion of quantitative contributions to traits by different factors in development. We avoid the pitfalls of previous accounts and argue that the distinction between innate and acquired traits is scientifically useful. We therefore address not only previous accounts of innateness but also skeptics about any account. The two are linked, in that a better account of innateness also enables us better to address the skeptics

Pre-emption cases may support, not undermine, the counterfactual theory of causation

Pre-emption cases have been taken by almost everyone to imply the unviability of the simple counterfactual theory of causation. Yet there is ample motivation from scientific practice to endorse a simple version of the theory if we can. There is a way in which a simple counterfactual theory, at least if understood contrastively, can be supported even while acknowledging that intuition goes firmly against it in pre-emption cases – or rather, only in some of those cases. For I present several new pre-emption cases in which causal intuition does not go against the counterfactual theory, a fact that has been verified experimentally. I suggest an account of framing effects that can square the circle. Crucially, this account offers hope of theoretical salvation – but only to the counterfactual theory of causation, not to others. Again, there is (admittedly only preliminary) experimental support for this account

Free will is not a testable hypothesis

Much recent work in neuroscience aims to shed light on whether we have free will. Can it? Can any science? To answer, we need to disentangle different notions of free will, and clarify what we mean by ‘empirical’ and ‘testable’. That done, my main conclusion is, duly interpreted: that free will is not a testable hypothesis. In particular, it is neither verifiable nor falsifiable by empirical evidence. The arguments for this are not a priori but rather are based on a posteriori consideration of the relevant neuroscientific investigations, as well as on standard philosophy of science work on the notion of testability

Genetic traits and causal explanation

I use a contrastive theory of causal explanation to analyze the notion of a genetic trait. The resulting definition is relational, an implication of which is that no trait is genetic always and everywhere. Rather, every trait may be either genetic or non-genetic, depending on explanatory context. I also outline some other advantages of connecting the debate to the wider causation literature, including how that yields us an account of the distinction between genetic traits and genetic dispositions

A dilemma for the Doomsday Argument

I present a new case in which the Doomsday Argument (‘DA’) runs afoul of epistemic intuition much more strongly than before. This leads to a dilemma: in the new case either DA is committed to unacceptable counterintuitiveness and belief in miracles, or else it is irrelevant. I then explore under what conditions DA can escape this dilemma. The discussion turns on several issues that have not been much emphasised in previous work on DA: a concern that I label trumping; the degree of uncertainty about relevant probability estimates; and the exact sequence in which we integrate DA and empirical concerns. I conclude that only given a particular configuration of these factors might DA still be of interest

Prediction, history and political science

To succeed, political science usually requires either prediction or contextual historical work. Both of these methods favor explanations that are narrow-scope, applying to only one or a few cases. Because of the difficulty of prediction, the main focus of political science should often be contextual historical work. These epistemological conclusions follow from the ubiquity of causal fragility, under-determination, and noise. They tell against several practices that are widespread in the discipline: wide-scope retrospective testing, such as much large-n statistical work; lack of emphasis on prediction; and resources devoted to ‘pure theory’ divorced from frequent empirical application. I illustrate, via Donatella della Porta’s work on political violence, the important role that is still left for theory. I conclude by assessing the scope for political science to offer policy advice

Harm and causation

I propose an analysis of harm in terms of causation: harm is when a subject is caused to be worse off. The pay-off from this lies in the details. In particular, importing influential recent work from the causation literature yields a contrastive-counterfactual account. This enables us to incorporate harm’s multiple senses into a unified scheme, and to provide that scheme with theoretical ballast. It also enables us to respond effectively to previous criticisms of counterfactual accounts, as well as to sharpen criticisms of rival views

Degree of explanation

Partial explanations are everywhere. That is, explanations citing causes that explain some but not all of an effect are ubiquitous across science, and these in turn rely on the notion of degree of explanation. I argue that current accounts are seriously deficient. In particular, they do not incorporate adequately the way in which a cause’s explanatory importance varies with choice of explanandum. Using influential recent contrastive theories, I develop quantitative definitions that remedy this lacuna, and relate it to existing measures of degree of causation. Among other things, this reveals the precise role here of chance, as well as bearing on the relation between causal explanation and causation itself