This is a summary of the 29 January session on Causality. Kishor presented on the institutional and academic practice of Causality in the fields of Market Research, Economics and Medicine, mostly focusing on the American part of the story.
(Note: Scattered source material)
Locate the discussion of causality in 3 specific disciplines with political dimensions – Market Research, Economics and Medicine
Other disciplines where also similar discussions could be located – Law, Psychology, Cognitive Science, Social Sciences
Each topic was discussed in two different ways: (I) Things that were happening historically within each of the disciplines, within the institutional perspectives and (II) Things that were happening on the outside of the disciplines, with an impact on things within the disciplines.
Overview of the philosophy of Causality
Aristotle, David Hume, John Stuart Mill were 3 key philosophers who laid down the basic philosophical framework of causality.
(a) Aristotle was asking the question: “What causes things to be?”
Four types of causes – Actual, Potential, Efficient and Teleological. Most of modern science is based on Efficient cause and Teleological cause. Efficient cause is about what moves something, or what makes something happen. Teleological cause is about to what end is it going towards. Post Darwinian revolution, modern sciences have been largely concerned about Teleological questions – such as how we got to where we are today, through a model of history.
(b) Hume has a different model.
3 basic principles that have been very influential in modern science. “A causes B” if (i) contiguity between A and B, (ii) inseparability – if A happens, B must necessarily happen, and (iii) temporal ordering, A has to happen before B. In the ‘Book of Why’, Judea Pearl talks about how Hume’s model sets the stage for his work on causality.
( c) Mill’s intuitive methods of typifying causality by going through various situations, as laid down in his book ‘System of Logic’.
Overall, figures like Hume and Mill have been highly influential on how methods from natural sciences got to be adapted to what are called ‘human sciences’. But we need to be careful against a philosophical over-determination on these grounds, since a lot of the actual disciplinary work around causality in the human sciences is also influenced by common sense, ordinary languages. In this sense, the disciplinary debates are best understood more as guidelines that practitioners have been following, rather than strict philosophical principles.
Begins as an offshoot of psychology starting from 1920s. Focused mainly on survey work, quantitative correlations, often quite simplistic statistical methods – aspiring to be ‘objective’ and ‘scientific’. Post WWII, there seems to be a shift towards more qualitative methods – could be because of new media technologies like the television. By 1980s, advertising was seen as much a discipline relying on quantitative methods, as on qualitative methods. From 2000s, powered by computers, more and more focus on complicated number crunching, network mappings, real time data analytics. This has taken us away from the regime of causality, and as a reaction, brought back the question of causality – in comparison with mere association mappings. ‘Why are things associated to one another?’ Of course, all this is going on in conversation with social science research. The cruder quantitative methods of early 20th Century coincide with structuralist paradigms in sociology, the move to qualitative coincide with more micro-turns in American social sciences, focused on questions of ‘who is doing the research’, ‘who is being studied or observed’, ‘who is the consumer’, etc. This was also effected by cross hirings between market research firms and social science researchers.
Not sure of the shift from quantitative to qualitative. There was a shift from the macro to the micro, but the micro also seems to be heavily quantitative, reliant on constant measurements. The neoliberal micro research also seems to have it’s own causal regime – such as something like ‘individual greed causes prosperity’.
What is the parallel story in India, particularly with regards to the cross-breeding between Indian social sciences and Indian market research.
Post WWII, 3 schools of economics in Western academia came up – MIT, Yale and Chicago. A key moment for the Yale school was the Cowles Commission – a movement to mathematize economic theory. While Keynes developed a theory that was amenable to mathematics, Cowles went much further. One of their distinction from the Chicago school was their emphasis on planning. Cowles was seen largely as the school that would triumph over the Keynesians. But eventually the Chicago school supplanted them both.
The Cowles Commission published monographs that dealt with the theory of causality. This was in connection with the Cold War-era rush towards science and technology research on both sides of the world – leading in turn to mathematizing economics as a ‘science’. For example, ‘Process Analytic approach’, ‘Structural Models approach’. The monographs published by the Cowles commission – such as those dealing with dependent and independent variables, noise, uncertainty, proofs, theorems, natural experiments, etc – are connected with some of what we discussed in the earlier sessions. Kenneth Arrow was a part of this project. This school laid the theoretical groundwork for economics as the discipline that it is today.
The other key part of the story is the Developmental school by people like Joan Robinson and others, who posited themselves against the Chicago school. The equilibrium style of economics – as espoused by the Chicago school, and other Israeli economists – is one of multiple ways of doing economics. But there have been people who opposed it. Developmental economics – at least parts of it – were for instance in opposition to the equilibrium style, and in fact also to an extent to the mathematization of economics. Kenneth Arrow, the Yale school, and some parts of the MIT school were somewhere in between. They were opposed to equilibrium theory, and instead help up the Keynesian principles based on planning – but in doing so, they heavily relied on quantifications and mathematization of the discipline. We should refrain thus from equating the mathematical approach with the equilibrium theory.
May be one way to categorize these different schools is to classify them in terms of a unidirectional, linear kind of causality, as opposed to a dialectical causality – the kind that Marx talked about. Rather than focusing on things like the extent of mathematization, or the take on equilibrium theory, and so on. For instance, one core aspect that will then have to be looked at is the idea of ‘contradictions’, and how these different schools dealt with the concept of contradictions as a major driving force of economic logic. One way to look at mathematization attempts could then be whether the mathematization is being done in order to properly describe those fundamental contradictions, or to somehow mathematically resolve those contradictions.
When we talked to practitioners in the field, the question of causality seems to come up in 2 different ways: (a) evidence-based research, and (b) big data driven approaches, specifically when it comes to personalized medicines for instance.
Evidence based approach: When a doctor practices medicine, they have to rely as much on scientific data (coming from scans, test reports, and so on), as on their own intuition and experience rooted in the local context. But post-1970s, partly because of the rise of health management systems such as insurance companies, private nursing homes, etc, the legal and regulatory pressures manifested in the push towards medical research that was ‘evidence based’, as opposed to the more intuitionist approaches. This is for example where the Randomized Control Trials system becomes strong. RCTs become an easy, ‘theory-neutral’ way to present one’s case in a scientific sense, without having to go through the extremely complicated and less understood biochemical causal pathways. Though RCT methods date back to as far as the 17th Century, post 1970 we see a spike in their importance.
Big data driven approach: This is a much more recent trend, such as in what is called as ‘metabolomics’ - “a systematic study of the unique chemical fingerprints that specific cellular processes leave behind”. Particularly since 2016-17, it has been argued that these big-data driven statistical measurements of the biochemical transactions in the body, when superposed with biochemical causal pathway diagrams – leads to a far better understanding about how the human body works. The market side of this story is the market-push towards personalized medicine, the technologies for which are currently very expensive.
The point about legal pressures pushing these disciplines like medicine or economics into certain kinds of ‘evidence-based sciences’ is interesting, and perhaps deserves more detailed inquiry. In particular, looking at the legal theater as a space where such social norms around causality, evidence, scientific rational, rational ethics, etc get engineered.
Two disciplines that kept coming up in course of preparing for this presentation were psychology (how do people’s minds make causal associations), and law (where it’s very anthropological – there is no legal definition of causality, and how causality gets established in a court thus varies from judge to judge, that is, it’s quite anthropological). There is no clear resolution of the question of causality in legal terms – which is why often times the key question in criminal proceedings is not whether someone’s actions ‘caused’ some damage, but rather whether the action was ‘irresponsible’. In other words, legality often has to convert questions of causality into questions of ethical norms. When it comes to causality in the legal domain, there are for instance distinct principles that are often in conflict with each other – for example, whether it was the action that caused something, or whether there was a mental decision behind that action that caused something, or between proximal causes and ultimate causes, and so on.
But in general, the fields such as those of technologies, law, etc seem to have a lot to do with what’s going on in these other fields such as medicine, market research, economics, etc. One example is perhaps the two different approaches to causality – inferential and a priori . Could this be a conflict between two classes – albeit small classes, such as the scientist versus the person who hires the scientist. This is in a similar spirit to Marx’s Paris manuscripts where he documents the conflict between the physiocrats and the monetarists on the question of land – the former looked at land as a distinct form of wealth, while the latter looked at it as just another form of capital – a conflict that was basically the class conflict between the feudal classes and the emerging capitalist classes. Can we say something similar about the conflict between the inferential and the a priori? For instance, some kind of ideological predisposition that scientists tend to operate with when it comes to the question of what knowledge is - ‘How does the world work? Why things are happening the way they are?’ This looks like some kind of class position. But people who hire them often don’t care about such questions = which is yet another class position.
We need to bring back the ‘political’ in this discussion of causality, which is where our earlier presentations on ‘structural causality’ become important. The way Althusser talks about ‘structure’, and how that ‘structure’ warps and informs the very questions we ask regarding causal relations, in other words the way the structure guides the underlying causal arguments – is worth keeping in mind. For example, when in economics they say ‘hierarchies are inevitable’ - that opens up a whole new paradigm of causal structures. And from this perspective, it is not clear what can be fundamentally new about this big data-based feedback mechanisms. Because what these inferential arguments do is to ignore structures – that is part of the political agenda itself. But if structures dictate causal paradigms, then more and more data is not going to effect any fundamental shift in such causal paradigms. This means we also have to be careful with ethical arguments. We perhaps need to move from ethical arguments, towards the discussion of the ‘political material structure’, and not just the so-called ‘ethical structure’.
Doesn’t causality in the positivist way reinforce structures of inequality? It does, but then it depends on what we mean by ‘positivist’. For example, this inference based formal epistemology is depoliticizing the debate, and in effect perpetuating inequality. For example, the complete depolitization of the Netherlands’ ethics for judges course.