Robert Northcott's Papers
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My research focuses on causal explanation, especially the notions of degree of causation and explanation. I use the machinery I have developed for that to address longstanding controversies in science, especially biology. I see the two aspects, namely philosophy of science and metaphysics, as necessarily developing in parallel, and as in turn informing and being informed by scientific practice. This requires work in all three areas: metaphysics, and both theoretical and applied philosophy of science.

I have received a National Science Foundation HPS Scholars award for spring and fall 2010 (worth $50,000), for the project 'Causation and evolution'. Before that, I received a University of Missouri Research Board award for spring and fall 2008 (worth $20,000), for the project 'Measuring causal strength in biology'. And  I received an UMSL Research Award worth $7,000 in fall 2006 for the project 'Causes in science and philosophy'. I have also received UMSL travel grants in each of the academic years 2005/6, 2006/7, 2007/8, 2008/9, and 2009/10.

(Quick links    In more detail    Works in progress    Popular articles)



Quick links

J11) Walsh on causes and evolution Philosophy of Science, 2010, 77.3: 457-467.
J10) ‘Verisimilitude: a causal approach’ Synthese, forthcoming. (This is a late draft.)
J9) ‘On Lewis, Schaffer and the non-reductive evaluation of counterfactuals’ Theoria, 2009, 75.4: 336-343. (This is a late draft.)
J8) ‘Is actual difference making actually different?’ Journal of Philosophy, 2009, 106.11: 629-634. (This is a late draft.)
J7) Natural-born determinists: a new defense of causation as probability-raising  Philosophical Studies, 2010, 50.1:1-20
J6) Weighted explanations in history  Philosophy of the Social Sciences 2008, 38.1: 76-96
J5) Can ANOVA measure causal strength?  Quarterly Review of Biology 2008, 83.1: 47-55
J4) Causation and contrast classes  Philosophical Studies 2008, 39.1: 111-123
J3) Causal efficacy and the analysis of variance  Biology and Philosophy 2006, 21.2: 253-276
J2) Pearson’s wrong turning: against statistical measures of causal efficacy  Philosophy of Science 2005, 72.5: 900-912
J1) Comparing apples with oranges  Analysis 2005, 65.1: 12-18

C2) Genetic traits and causal explanation  to appear in Thomas Reydon and Katie Plaisance (eds) Philosophy of Behavioral Biology (Springer: Boston Studies in Philosophy of Science)
C1) Progress in economics  chapter 11 in Oxford Handbook of Philosophy of Economics (Oxford 2009, Don Ross and Harold Kincaid, eds), pp306-337
(co-authored with Anna Alexandrova)

S3) How necessary are randomized controlled trials?  to appear in the 9th edition of Ronald Munson, Intervention and Reflection: Basic Issues in Medical Ethics (Thomson Wadsworth)
S2) Review of ‘The Scientific Study of Society’ by Max Steuer  Economics and Philosophy 2004, 20.2: 375-381

P2) Bad luck or the ref's fault?  in Ted Richards (ed), Soccer and Philosophy (Open Court, 2010), pp319-326
P1) The Irrational Game: why there’s no perfect system in Eric Bronson (ed), Poker and Philosophy (Open Court, 2006), pp105-115


In more detail

J11) Walsh on causes and evolution
Philosophy of Science, 77.3, July 2010, pp457-467.
Denis Walsh (2007) wrote a striking defense in Philosophy of Science of the statisticalist (i.e. non-causalist) position regarding the forces of evolution. I defend the causalist view against his objections. I argue that the heart of the issue lies in the nature of non-additive causation. Detailed consideration of that turns out to defuse Walsh’s ‘description-dependence’ critique of causalism. Nevertheless, the critique does suggest a basis for reconciliation between the two competing views

J10) Verisimilitude: a causal approach (This is a late draft.)
Synthese, forthcoming.
I present a new definition of verisimilitude, framed in terms of causes. Roughly speaking, according to it a scientific model is approximately true if it captures accurately the strengths of the causes present in any given situation. Against much of the literature, I argue that any satisfactory account of verisimilitude must inevitably restrict its judgments to context-specific models rather than general theories. We may still endorse - and only need - a relativized notion of scientific progress, understood now not as global advance but rather as the mastering of particular problems. This also sheds new light on longstanding difficulties surrounding language-dependence, and models committed to false ontologies.

J9) On Lewis, Schaffer and the non-reductive evaluation of counterfactuals (This is a late draft.)
Theoria, forthcoming (published online November 2009).
In a 2004 Analysis article, Jonathan Schaffer proposes an ingenious amendment to David Lewis’s semantics for counterfactuals. This amendment explicitly invokes the notion of causal independence, thus giving up Lewis’s ambitions for a reductive counterfactual account of causation. But in return, it rescues Lewis’s semantics from extant counterexamples. I present a new counterexample that defeats even Schaffer’s amendment. Further, I argue that a better approach would be to follow the causal modelling literature and evaluate counterfactuals via an explicit postulated causal structure. This alternative approach easily resolves the new counterexample, as well as all the previous ones. Up to now, its perceived drawback relative to Lewis’s scheme has been its non-reductiveness. But since the same drawback applies equally to Schaffer’s amended scheme, this becomes no longer a point of comparative disadvantage.

J8) Is actual difference making actually different? (This is a late draft.)
Journal of Philosophy, 106.11, November 2009, pp629-634.
This paper responds to Kenneth Waters’s recent 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.

J7) Natural-born determinists: a new defense of causation as probability-raising
    Philosophical Studies, 50.1, August 2010, pp1-20.
A definition of causation as probability-raising is threatened by two kinds of counterexample: first, when a cause lowers the probability of its effect; and second, when the probability of an effect is raised by a non-cause. I present an account that deals successfully with problem cases of both these kinds. In doing so, I also explore some implications of incorporating into the metaphysical investigation considerations of causal psychology. In particular, if we interpret the formal account as a theory of causal judgment rather than of causation itself, that enables us indirectly to defend a slightly different, and more desirable, metaphysical account than otherwise. The psychological detour thus pays metaphysical dividends.

J6) Weighted explanations in history   
    Philosophy of the Social Sciences 38.1, March 2008, pp76-96
Weighted explanations, whereby some causes are deemed more or less important than others, are ubiquitous in historical studies and indeed everyday life. But it turns out that furnishing a good account of them is a surprisingly delicate task, and one so far treated either unsatisfactorily or not at all in the explanation and philosophy of history literatures. As a result, it is still unclear exactly what a historian is claiming when offering a weighted explanation, and also unclear what kinds of evidence are relevant to assessing such claims. Drawing from influential recent work on causation and causal explanation, I develop a new definition of causal-explanatory strength. This yields a principled way to incorporate pragmatic aspects of explanation, and makes clear exactly which aspects of explanatory weighting are subjective and which objective. One payoff is that many widespread claims and assumptions regarding weighted explanations are now revealed, surprisingly, to be either false or confused.

J5) Can ANOVA measure causal strength?
    Quarterly Review of Biology 83.1, March 2008, pp47-55
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.

J4) Causation and contrast classes
    Philosophical Studies 39.1, May 2008, pp111-123
I argue that causation is a contrastive relation: c-rather-than-C* causes e-rather-than-E*, where C* and E* are contrast classes associated respectively with actual events c and e. I explain why this is an improvement on the traditional binary view, and develop a detailed definition. It turns out that causation is only well defined in ‘uniform’ cases, where either all or none of the members of C* are related appropriately to members of E*.

J3) Causal efficacy and the analysis of variance
    Biology and Philosophy 21.2, March 2006, pp253-276
The causal impact 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 reponsible 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
2) a superior alternative definition is available that readily reconciles both the entangled-singleton and the population-variation senses of causal efficacy.

J2) Pearson’s wrong turning: against statistical measures of causal efficacy
    Philosophy of Science 72.5, December 2005, pp900-912
Standard statistical measures of strength of association, although pioneered by Pearson deliberately to be acausal, nowadays are routinely used to measure causal efficacy. But their acausal origins have left them ill suited to this latter purpose. I distinguish between two different conceptions of causal efficacy, and argue that:
1) Both conceptions can be useful
2) The statistical measures only attempt to capture the first of them
3) They are not fully successful even at this
4) An alternative definition based more squarely on causal thinking not only captures the second conception, it can also capture the first one better too.

J1) Comparing apples with oranges
   Analysis 65.1, January 2005, pp12-18
'If two men lay bricks to build a wall, we may quite fairly measure their contributions by counting the number laid by each; but if one mixes the mortar and the other lays the bricks, it would be absurd to measure their relative quantitative contributions by measuring the volumes of bricks and of mortar' (Richard Lewontin). Thus: 'For it to make sense to ask what (or how much) a cause contributes to an effect, the various causes must be commensurable in the way they produce their effects' (Elliott Sober). These claims sound reasonable but I show on the contrary that, for their contributions to be comparable, it is neither necessary nor sufficient that two causes also be commensurable. Rather, in a sense that I discuss, what really matters is that they be separable.


C2) Genetic traits and causal explanation  to appear in Thomas Reydon and Katie Plaisance (eds) Philosophy of Behavioral Biology (Springer: Boston Studies in Philosophy of Science)
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.

C1) Progress in economics
    chapter 11 in Oxford Handbook of Philosophy of Economics (Oxford 2009, Don Ross and Harold Kincaid, eds), pp306-337
    (co-authored with Anna Alexandrova)
The 1994 US spectrum auction  is now a paradigmatic case of the successful use of microeconomic theory for policy-making. We use a detailed analysis of it to review standard accounts in philosophy of science of how idealized models are connected to messy reality. We show that in order to understand what made the design of the spectrum auction successful, a new such account is required, and we present it here. Of especial interest is the light this sheds on the issue of progress in economics. In particular, it enables us to get clear on exactly what has been progressing, and on exactly what theory has – and has not – contributed to that. This in turn has important implications for just what it is about economic theory that we should value.

Some works in progress and under review
I am very happy to send these papers upon request.

-- ‘Explanatory strength’. This paper is the culmination of a research interest of mine of several years, namely how best to analyze individual causes explaining outcomes only partially, and, closely related to that, how causal responsibility for some outcome should be shared out between different causes. I have found the issue to be ubiquitous across science. Here, I extend standard approaches by tackling explicitly - for a range of cases - the relatively neglected issue of explanandum-dependence. This establishes a novel distinction, between what I label causal strength and explanatory strength. Among other benefits, it also sheds new light  on the relation between causal explanation and causation itself.

-- ‘Causation and genetic drift’. I argue that most existing analyses of drift either exaggerate the extent to which it is (causal-)explanatory, or else err the opposite way by denying that it can be explanatory at all.

-- ‘Symmetric overdetermination’. I present a novel analysis of this venerable paradox that grows out of my work on causal strength, in particular when that is applied to situations of non-additive causal interaction.

-- Philosophical methodology. I am interested in what experimental philosophy might or might not be able to contribute to debates in metaphysics. I explore this in the context of a couple of issues about causation.
Popular articles
(If done well, I think popular articles can be admirable ways of engaging a wider public with philosophy, in much the same way as good teaching can be. Alas, they are not always done well. But I hope in mine to use familiar contexts to introduce several metaphysical issues without distortion, and without tediously claiming more for philosophy than it actually delivers.)

P2) Bad luck or the ref's fault?
    in Ted Richards (ed), Soccer and Philosophy (Open Court, 2010), pp319-326
I discuss classic issues surrounding luck, determinism and probability in the context of the penalty shoot-outs used in football’s World Cup. Can it ever make objective sense to blame an outcome on bad luck? I go on to discuss whether we can legitimately pin the blame on any one factor at all, such as a referee. This takes us into issues surrounding the apportioning of causal responsibility.

P1) The Irrational Game: why there’s no perfect system
    in Eric Bronson (ed), Poker and Philosophy (Open Court, 2006), pp105-115
I use poker as a convenient illustration of probability, determinism and counterfactuals. More originally, I also discuss the roles of rationality versus psychological hunches, and explain why even in principle game theory cannot provide us the panacea of a perfect winning srategy.
(N.B. The document I link to here is slightly longer than the abbreviated version that appears in the book, and also differs in a few other minor details.)


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