Ontological emergence

10 Jul, 2021 at 10:37 | Posted in Theory of Science & Methodology | 4 Comments

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Wittgensteins Sprachphilosophie — der Fliege den Ausweg aus dem Fliegenglas zeigen

4 Jul, 2021 at 16:43 | Posted in Theory of Science & Methodology | Leave a comment

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David Graeber on the importance of Roy Bhaskar’s work

3 Jul, 2021 at 11:09 | Posted in Theory of Science & Methodology | Leave a comment

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No philosopher of science has influenced yours truly’s thinking more than Roy Bhaskar did. Roy always emphasised that the world itself should never be conflated with the knowledge we have of it. Science can only produce meaningful, relevant and realist knowledge if it acknowledges its dependence of the​ world out there. Ultimately that also means that the critique yours truly wages against mainstream economics is that it doesn’t take that ontological requirement seriously.

On the limits of formal methods in causal inference

2 Jul, 2021 at 22:08 | Posted in Theory of Science & Methodology | Leave a comment

Causal Inference: Introduction to Causal Effect Estimation | inovex GmbHOur problem is … with the temptation to think that by stating some of our assumptions more clearly, we have successfully formalized the entire inferential process … Science may indeed seek objectivity, and for this reason a deductive method for causal inference is indeed highly desirable. But this does not mean that it is possible: we cannot have one just because we decide we need one. Causal conclusions do not follow deductively from data without a strong set of auxiliary assumptions, and … these assumptions are themselves not deductive consequences of the data. A formal method may indeed be extremely helpful, provided that its significance is not misunderstood and its dependence on supporting assumptions not forgotten …

If it is claimed that causal inference has been formalized and it is not explained that the formalism, powerful as it may be, is only as good as the assumptions that support it, then causal conclusions will look surer (‘more objective’) than they really are …

Estimations either of counterfactual contrasts or of interventions are interesting and important, but are often local effects in a particular time, place and population. And even these are not pure empirical findings, but are heavily theory-laden. They are not read or calculated from data, but inferred from it, and the inference depends upon a huge network of background hypotheses and scientific knowledge … Thus, causality is not a statistical concept whose presence or absence can be determined by statistical analysis of a set of data. It is a theoretical concept, even when invoked in quantitative estimates for particular populations. As  with any scientific theoretical finding, we infer causal conclusions (including estimations of causal effect) as the result of an inductive inference, considering all the available evidence.

Alex Broadbent, Jan P Vandenbroucke, Neil Pearce

Moving beyond induction and deduction

2 Jul, 2021 at 10:08 | Posted in Theory of Science & Methodology | 8 Comments

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In a time when scientific relativism is expanding, it is important to keep up the claim for not reducing science to a pure discursive level. We have to maintain the Enlightenment tradition in which the main task of science is studying the structure of reality.

Science is made possible by the fact that there are structures that are durable and independent of our knowledge or beliefs about them. There exists a reality beyond our theories and concepts of it. Contrary to positivism, yours truly would as a critical realist argue that the main task of science is not to detect event-regularities between observed facts, but rather to identify and explain the underlying structurex/forces/powers/ mechanisms that produce the observed events.

Given that what we are looking for is to be able to explain what is going on in the world we live in, it would — instead of building models based on logic-axiomatic, topic-neutral, context-insensitive and non-ampliative deductive reasoning, as in mainstream economic theory — be so much more fruitful and relevant to apply inference to the best explanation.

In science we standardly use a logically non-valid inference — the fallacy of affirming the consequent — of the following form:

(1) p => q
(2) q
————-
p

or, in instantiated form

(1) ∀x (Gx => Px)

(2) Pa
————
Ga

Although logically invalid, it is nonetheless a kind of inference — abduction — that may be factually strongly warranted and truth-producing.

Following the general pattern ‘Evidence  =>  Explanation  =>  Inference’ we infer something based on what would be the best explanation given the law-like rule (premise 1) and an observation (premise 2). The truth of the conclusion (explanation) is nothing that is logically given, but something we have to justify, argue for, and test in different ways to possibly establish with any certainty or degree. And as always when we deal with explanations, what is considered best is relative to what we know of the world. In the real world, all evidence is relational (e only counts as evidence in relation to a specific hypothesis H) and has an irreducible holistic aspect. We never conclude that evidence follows from a hypothesis simpliciter, but always given some more or less explicitly stated contextual background assumptions. All non-deductive inferences and explanations are necessarily context-dependent.

If we extend the abductive scheme to incorporate the demand that the explanation has to be the best among a set of plausible competing potential and satisfactory explanations, we have what is nowadays usually referred to as inference to the best explanation.

In inference to the best explanation we start with a body of (purported) data/facts/evidence and search for explanations that can account for these data/facts/evidence. Having the best explanation means that you, given the context-dependent background assumptions, have a satisfactory explanation that can explain the evidence better than any other competing explanation — and so it is reasonable to consider the hypothesis to be true. Even if we (inevitably) do not have deductive certainty, our reasoning gives us a license to consider our belief in the hypothesis as reasonable.

Accepting a hypothesis means that you believe it does explain the available evidence better than any other competing hypothesis. Knowing that we — after having earnestly considered and analysed the other available potential explanations — have been able to eliminate the competing potential explanations, warrants and enhances the confidence we have that our preferred explanation is the best explanation, i. e., the explanation that provides us (given it is true) with the greatest understanding.

What is Inference? | Ontotext Fundamentals SeriesThis, of course, does not in any way mean that we cannot be wrong. Of course, we can. Inferences to the best explanation are fallible inferences — since the premises do not logically entail the conclusion — so from a logical point of view, inference to the best explanation is a weak mode of inference. But if the arguments put forward are strong enough, they can be warranted and give us justified true belief, and hence, knowledge, even though they are fallible inferences. As scientists we sometimes — much like Sherlock Holmes and other detectives that use inference to the best explanation reasoning — experience disillusion. We thought that we had reached a strong conclusion by ruling out the alternatives in the set of contrasting explanations. But — what we thought was true turned out to be false.

That does not necessarily mean that we had no good reasons for believing what we believed. If we cannot live with that contingency and uncertainty, well, then we are in the wrong business. If it is deductive certainty you are after — rather than the ampliative and defeasible reasoning in inference to the best explanation — well, then get into math or logic, not science.

Sir. Austin Bradford Hill (1897-1991). | Download Scientific Diagram What I do not believe — and this has been suggested — is that we can usefully lay down some hard-and-fast rules of evidence that must be obeyed before we accept cause and effect. None of my nine viewpoints can bring indisputable evidence for or against the cause-and-effect hypothesis and none can be required as a sine qua non. What they can do, with greater or less strength, is to help us to make up our minds on the fundamental question – is there any other way of explaining the set of facts before us, is there any other answer equally, or more, likely than cause and effect?

Austin Bradford Hill

The man who stopped smoking and saved millions of lives

1 Jul, 2021 at 16:53 | Posted in Theory of Science & Methodology | 3 Comments

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Do RCTs really carry special epistemic weight?

30 Jun, 2021 at 17:09 | Posted in Theory of Science & Methodology | Leave a comment

Mike Clarke, the Director of the Cochrane Centre in the UK, for example, states on the Centre’s Web site: ‘In a randomized trial, the only difference between the two groups being compared is that of most interest: the intervention under investigation’.

Evidence-based medicine is broken: why we need data and technology to fix itThis seems clearly to constitute a categorical assertion that by randomizing, all other factors — both known and unknown — are equalized between the experimental and control groups; hence the only remaining difference is exactly that one group has been given the treatment under test, while the other has been given either a placebo or conventional therapy; and hence any observed difference in outcome between the two groups in a randomized trial (but only in a randomized trial) must be the effect of the treatment under test.

Clarke’s claim is repeated many times elsewhere and is widely believed. It is admirably clear and sharp, but it is clearly unsustainable … Clearly the claim taken literally is quite trivially false: the experimental group contains Mrs Brown and not Mr Smith, whereas the control group contains Mr Smith and not Mrs Brown, etc. Some restriction on the range of differences being considered is obviously implicit here; and presumably the real claim is something like that the two groups have the same means and distributions of all the [causally?] relevant factors. Although this sounds like a meaningful claim, I am not sure whether it would remain so under analysis … And certainly, even with respect to a given (finite) list of potentially relevant factors, no one can really believe that it automatically holds in the case of any particular randomized division of the subjects involved in the study. Although many commentators often seem to make the claim … no one seriously thinking about the issues can hold that randomization is a sufficient condition for there to be no difference between the two groups that may turn out to be relevant …

In sum, despite what is often said and written, no one can seriously believe that having randomized is a sufficient condition for a trial result to be reasonably supposed to reflect the true effect of some treatment. Is randomizing a necessary condition for this? That is, is it true that we cannot have real evidence that a treatment is genuinely effective unless it has been validated in a properly randomized trial? Again, some people in medicine sometimes talk as if this were the case, but again no one can seriously believe it. Indeed, as pointed out earlier, modern medicine would be in a terrible state if it were true. As already noted, the overwhelming majority of all treatments regarded as unambiguously effective by modern medicine today — from aspirin for mild headache through diuretics in heart failure and on to many surgical procedures — were never (and now, let us hope, never will be) ‘validated’ in an RCT.

John Worrall

Testing causal claims

27 Jun, 2021 at 14:03 | Posted in Theory of Science & Methodology | Comments Off on Testing causal claims

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What does randomisation guarantee? Nothing!

26 Jun, 2021 at 17:43 | Posted in Theory of Science & Methodology | 3 Comments

BJUP interview with John Worrall | Philosophy, Logic and Scientific MethodDoes not randomization somehow or other guarantee (or perhaps, much more plausibly, provide the nearest thing that we can have to a guarantee) that any possible links to … outcome, aside from the link to treatment …, are broken?

Although he does not explicitly make this claim, and although there are issues about how well it sits with his own technical programme, this seems to me the only way in which Pearl could, in the end, ground his argument for randomizing. Notice, first, however, that even if the claim works then it would provide a justification, on the basis of his account of cause, only for randomizing after we have deliberately matched for known possible confounders … Once it is accepted that for any real randomized allocation known factors might be unbalanced — and more sensible defenders of randomization do accept this (though curiously, as we saw earlier, they recommend rerandomizing until the known factors are balanced rather than deliberately balancing them!) — then it seems difficult to deny that a properly matched experimental and control group is better, so far as preventing known confounders from producing a misleading outcome, than leaving it to the happenstance of the tosses …

The random allocation may ‘sever the link’ with this unknown factor or it may not (since we are talking about an unknown factor, then, by definition, we will not and cannot know which). Pearl’s claim that Fisher’s method ‘guarantees’ that the link with the possible confounders is broken is then, in practical terms, pure bluster. 

John Worrall

The point of making a randomized experiment is often said to be that it ‘ensures’ that any correlation between a supposed cause and effect indicates a causal relation. This is believed to hold since randomization (allegedly) ensures that a supposed causal variable does not correlate with other variables that may influence the effect.

The problem with that (rather simplistic) view on randomization is that the claims made are both exaggerated and strictly seen false:

• Even if you manage to do the assignment to treatment and control groups ideally random, the sample selection certainly is — except in extremely rare cases — not random. Even if we make a proper randomized assignment, if we apply the results to a biased sample, there is always the risk that the experimental findings will not apply. What works ‘there,’ does not work ‘here.’ Randomization hence does not ‘guarantee ‘ or ‘ensure’ making the right causal claim. Although randomization may help us rule out certain possible causal claims, randomization per se does not guarantee anything!

• Even if both sampling and assignment are made in an ideal random way, performing standard randomized experiments only give you averages. The problem here is that although we may get an estimate of the ‘true’ average causal effect, this may ‘mask’ important heterogeneous effects of a causal nature. Although we get the right answer of the average causal effect being 0, those who are ‘treated’ may have causal effects equal to -100 and those ‘not treated’ may have causal effects equal to 100. Contemplating being treated or not, most people would probably be interested in knowing about this underlying heterogeneity and would not consider the average effect particularly enlightening.

• There is almost always a trade-off between bias and precision. In real-world settings, a little bias often does not overtrump greater precision. And — most importantly — in case we have a population with sizeable heterogeneity, the average treatment effect of the sample may differ substantially from the average treatment effect in the population. If so, the value of any extrapolating inferences made from trial samples to other populations is highly questionable.

• Since most real-world experiments and trials build on performing a single randomization, what would happen if you kept on randomizing forever, does not help you to ‘ensure’ or ‘guarantee’ that you do not make false causal conclusions in the one particular randomized experiment you actually do perform. It is indeed difficult to see why thinking about what you know you will never do, would make you happy about what you actually do.

The problem many ‘randomistas’ end up with when underestimating heterogeneity and interaction is not only an external validity problem when trying to ‘export’ regression results to different times or different target populations. It is also often an internal problem to the millions of regression estimates that economists produce every year.

‘Ideally controlled experiments’ tell us with certainty what causes what effects — but only given the right ‘closures.’ Making appropriate extrapolations from (ideal, accidental, natural, or quasi) experiments to different settings, populations, or target systems, is not easy. And since trials usually are not repeated, unbiasedness and balance on average over repeated trials say nothing about anyone trial. ‘It works there’ is no evidence for ‘it will work here.’ Causes deduced in an experimental setting still have to show that they come with an export-warrant to the target population/system. The causal background assumptions made have to be justified, and without licenses to export, the value of ‘rigorous’ and ‘precise’ methods — and ‘on-average-knowledge’ — is despairingly small.

RCTs have very little reach beyond giving descriptions of what has happened in the past. From the perspective of the future and for policy purposes they are as a rule of limited value since they cannot tell us what background factors were held constant when the trial intervention was being made.

RCTs usually do not provide evidence that the results are exportable to other target systems. RCTs cannot be taken for granted to give generalisable results. That something works somewhere for someone is no warranty for us to believe it to work for us here or even that it works generally.

Randomisation may often — in the right contexts — help us to draw causal conclusions. But it certainly is not necessary to secure scientific validity or establish causality. Randomisation guarantees nothing. Just as observational studies may be subject to different biases, so are randomised studies and trials.

The epistemic fallacy

19 Jun, 2021 at 15:48 | Posted in Theory of Science & Methodology | 15 Comments

bhaskit is not the fact that science occurs that gives the world a structure such that it can be known by men. Rather, it is the fact that the world has such a structure that makes science, whether or not it actually occurs, possible. That is to say, it is not the character of science that imposes a determinate pattern or order on the world; but the order of the world that, under certain determinate conditions, makes possible the cluster of activities we call ‘science’. It does not follow from the fact that the nature of the world can only be known from (a study of) science, that its nature is determined by (the structure of) science. Propositions in ontology, i.e. about being, can only be established by reference to science. But this does not mean that they are disguised, veiled or otherwise elliptical propositions about science … The ‘epistemic fallacy’ consists in assuming that, or arguing as if, they are.

No philosopher of science has influenced yours truly’s thinking more than Roy did, and in a time when scientific relativism is still on the march, it is important to keep up his claim for not reducing science to a pure discursive level.

Roy-Bhaskar-009

Science is made possible by the fact that there exists a reality beyond our theories and concepts of it. It is this reality that our theories in some way deal with. Contrary to positivism, I cannot see that the main task of science is to detect event-regularities between observed facts. Rather, the task must be conceived as identifying the underlying structure and forces that produce the observed events.

The problem with positivist social science is not that it gives the wrong answers, but rather that in a strict sense it does not give answers at all. Its explanatory models presuppose that the social reality is ‘closed,’ and since social reality is fundamentally ‘open,’ models of that kind cannot explain anything about​ what happens in such a universe. Positivist social science has to postulate closed conditions to make its models operational and then – totally unrealistically – impute these closed conditions to society’s real structure.

What makes knowledge in social sciences possible is the fact that society consists of social structures and positions that influence the individuals of society, partly through their being the necessary prerequisite for the actions of individuals but also because they dispose individuals to act (within a given structure) in a certain way. These structures constitute the ‘deep structure’ of society.

Our observations and theories are concept-dependent without therefore necessarily being concept-determined. There is a reality existing independently of our knowledge and theories of it. Although we cannot apprehend it without using our concepts and theories, these are not the same as reality itself. Reality and our concepts of it are not identical. Social science is made possible by existing structures and relations in society that are continually reproduced and transformed by different actors.

Explanations and predictions of social phenomena require theory constructions. Just looking for correlations between events is not enough. One has to get under the surface and see the deeper underlying structures and mechanisms that essentially constitute the social system.

The basic question one has to pose when studying social relations and events are​ what are the fundamental relations without which they would cease to exist. The answer will point to causal mechanisms and tendencies that act in the concrete contexts we study. Whether these mechanisms are activated and what effects they will have in that case it is not possible to predict, since these depend on accidental and variable relations. Every social phenomenon is determined by a host of both necessary and contingent relations, and it is impossible in practice to have complete knowledge of these constantly changing relations. That is also why we can never confidently predict them. What we can do, through learning about the mechanisms of the structures of society, is to identify the driving forces behind them, thereby making it possible to indicate the direction in which things tend to develop.

The world itself should never be conflated with the knowledge we have of it. Science can only produce meaningful, relevant and realist knowledge if it acknowledges its dependence of the​ world out there. Ultimately that also means that the critique yours truly wages against mainstream economics is that it doesn’t take that ontological requirement seriously.

Challenging causal models — the low birth weight paradox

4 Jun, 2021 at 17:20 | Posted in Theory of Science & Methodology | Comments Off on Challenging causal models — the low birth weight paradox

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Lesson learned? Beware of collider bias and ‘censored’ data!

Is causality only in the mind?

31 May, 2021 at 10:35 | Posted in Theory of Science & Methodology | 3 Comments

James HeckmanI make two main points that are firmly anchored in the econometric tradition. The first is that causality is a property of a model of hypotheticals. A fully articulated model of the phenomena being studied precisely defines hypothetical or counterfactual states. A definition of causality drops out of a fully articulated model as an automatic by-product. A model is a set of possible counterfactual worlds constructed under some rules. The rules may be the laws of physics, the consequences of utility maximization, or the rules governing social interactions, to take only three of many possible examples. A model is in the mind. As a consequence, causality is in the mind.

James Heckman

So, according to this ‘Nobel prize’ winning econometrician, “causality is in the mind.” But is that a tenable view? Yours truly thinks not. If one as an economist or social scientist would subscribe to that view there would be pretty little reason to be interested in questions of causality at all.  And it sure doesn’t suffice just to say that all science is predicated on assumptions. To most of us, models are seen as ‘vehicles’ or ‘instruments’ by which we represent causal processes and structures that exist and operate in the real world. As we all know, models often do not succeed in representing or explaining these processes and structures, but if we didn’t consider them as anything but figments of our minds, well then maybe we ought to reconsider why we should be in the science business at all …

The world as we know it has limited scope for certainty and perfect knowledge. Its intrinsic and almost unlimited complexity and the interrelatedness of its parts prevent the possibility of treating it as constituted by atoms with discretely distinct, separable and stable causal relations. Our knowledge accordingly has to be of a rather fallible kind. To search for deductive precision and rigour in such a world is self-defeating. The only way to defend such an endeavour is to restrict oneself to prove things in closed model-worlds. Why we should care about these and not ask questions of relevance is hard to see. As scientists we have to get our priorities right. Ontological under-labouring has to precede epistemology.

The value of getting at precise and rigorous conclusions about causality based on ‘tractability’ conditions that are seldom met in real life, is difficult to assess. Testing and constructing models is one thing, but we do also need guidelines for how to evaluate in which situations and contexts they are applicable. Formalism may help us a bit down the road, but we have to make sure it somehow also fits the world if it is going to be really helpful in navigating that world. In all of science, conclusions are never more certain than the assumptions on which they are founded. But most epistemically convenient methods and models that work in ‘well-behaved’ systems do not come with warrants that they will work in other (real-world) contexts.

Science manufacturing ignorance

25 May, 2021 at 08:16 | Posted in Theory of Science & Methodology | Comments Off on Science manufacturing ignorance

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Racial bias in the use of force by police

16 May, 2021 at 23:22 | Posted in Theory of Science & Methodology | 3 Comments

UN Committee condemns U.S. for racial disparity, police brutality | PBS  NewsHourOur analysis indicates that existing empirical work in this area is producing a misleading portrait of evidence as to the severity of racial bias in police behavior. Replicating and extending the study of police behavior in New York in Fryer (2019), we show that the consequences of ignoring the selective process that generates police data are severe, leading analysts to dramatically underestimate or conceal entirely the differential police violence faced by civilians of color. For example, while a naïve analysis that assumes no race-based selection into the data suggests only 10,000 black and Hispanic civilians were handcuffed because of racial bias in New York City between 2003 and 2013, we estimate that the true number is approximately 56,000. And while analyses ignoring bias in stopping would conclude that 10% of uses of force against black and Hispanic civilians in these data were discriminatory, after bias-correction, we estimate that the true percentage is 39% …

Traditionally, analysts use data on stopped individuals to study bias by computing the difference in violence rates between stopped minority and white civilians, while controlling for observable differences between these two sets of encounters. We term this the “naïve estimator” … However, without further assumptions, this quantity will have no causal interpretation so long as the treatment affects the mediator (i.e., civilian race affects whether officers detain a civilian). As we show below, this is because treated encounters (with minority civilians) that result in a stop will not be comparable to those with stopped control (majority) civilians. As a simple example, suppose officers exhibited racial bias as follows: they detain white civilians if they observe them committing a serious crime (such as assault, potentially warranting the use of force) but detain nonwhite civilians regardless of observed behavior. When this is true, comparing stopped white and nonwhite civilians amounts to comparing fundamentally different groups. The analyst will observe force used against a greater proportion of stopped white civilians because of the differential physical threat they pose to officers. Under the traditional approach, the analyst would naïvely conclude that anti-white bias exists, yielding an erroneous portrait of racial discrimination in the use of force.

Dean Knox, Will Lowe, Jonathan Mummolo

This study is a must read for every researcher trying to identify causal relations from proprietary administrative data sets!

Looking only at data often give the wrong causal impression — especially when, as in this case, the data is loaded right from the start and results in sample selection bias due to post-treatment conditioning. Comparing white bank robbers to black civilians committing no crime does not give us the apples to apples comparison needed for making causal inferences.

Data — no matter if ‘big’ or not — never by itself give us credible causal inferences.

From association to causation

11 May, 2021 at 18:15 | Posted in Theory of Science & Methodology | Comments Off on From association to causation

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