Big data are getting a popular and fashionable topic in philosophy of science, and for a number of good reasons. One reason, I think, is that big data practices in the social sciences push us to rethink the notion of objectivity. At least, this is the line of argument I’m trying to develop with Jean-Christophe Plantin.
… or why mentoring young academics, whether women or men? And what to tell them? There’s quite a lot of material and discussion on the topic. Just google, you’ll find out.
So, a quick reflection on what I did today. I’ve been invited to lead a workshop on how to get your paper published, as part of a the Conference by Women in Philosophy 3# , organised entirely by MA and PhD students from the University of Amsterdam and the Free University of Amsterdam. Kudos to the Ladies there, they have done a great job!
What you’ll find in the slides is probably all known to you. Or perhaps not all. There are a few of things that I’d like to emphasise. First, we’d better publish papers with positive results, rather than just negative results. Second, we’d better write referee reports that are constructive, rather than just destructive. Third, none of our results is just ‘ours’ so we’d better do things collegially, at all stages of the publishing process.
This is the way I try to train my students in writing papers and in raising points for discussion at seminars. This is the way I try to write my papers and referee reports. This is the way I try to carry out editorial work.
This is not independent from a certain meta-philosophical stance that I tried to develop lately and that I presented at some conferences recently (more info to follow soon). And not even this is just my own idea, but the result of years of collaborations with a several people, especially my friend and colleague Phyllis Illari.
And now, go enjoy philosophy!
Better late than never, here is a quick report on my brief visit to the Universidad Nacional Autónoma de México in April 2015 — a very kind invitation by Atocha Aliseda and Fernanda Samaniego. It has been an intense week, filled with marvellous discussions with colleagues and students at the Instituto de Investigaciones Filosóficas, meetings with good old friends from the Canterbury times, and even with some sightseeing — in Mexico City, I visited the Museo Interactivo de Economia and the Museo Nacional de Arte.
My Monday lecture was part of the Seminar on Logic and Heuristic organised by Atocha. The themes of their seminar is much broader than what the title suggests. I presented some ideas about philosophy of medicine, specifically how to understand medicine (as an umbrella term that includes several types of medical practices) and what kind of philosophy of science questions I consider interesting therein. Part of what I presented at the seminar is work in progress with Brendan Clarke.
On Tuesday I have a long lecture on causal modelling in the social sciences. This was part of an MA course on explanation, so the focus was what we can explain using causal models in social research, and how. I tried to condense much of what I know about causal modelling in these slides. A lot of what I presented is in my first monograph (Causality and Causal Modelling in the Social Sciences. Measuring Variations. Springer 2009) and in some more recent papers. Students asked plenty of questions — it has been a pleasure lecturing them!
Finally, my Wednesday talk was part of the seminar of the Institute: a broad and heterogeneous audience indeed! I presented what we might call causal pluralism 2.0. This is joint work with Phyllis Illari. Together we wrote a monograph on causality (Causality: Philosophical Theory Meets Scientific Practice, OUP 2014), the aim of which is to put some order in the vast philosophical literature on causation. Our goal, however, goes beyond offering just an introduction to causality. We also offer a view on how all these strands, pieces of the literature may (or should) stand together. We defend a qualified version of causal pluralism, that we explain in analogy with building a mosaic. More explanation in the slides below, and in chapters 23-24 of our book!
I’m so grateful to Atocha and Fernanda for the fantastic occasion to present my work, but mostly for their fantastic hospitality. They really made me feel home. I hope to go back some time soon!
In the middle of the UvA protest, of a flu, and of an intensive semester filled with teaching, I escaped to beautiful Venice — the Amsterdam of the South ! — to talk about various things, among which causality, modelling, and computer science.
I delivered the first lecture at the Department of Management of Ca’ Foscari. The talk is based on some joint work done with my friend and colleague Alessio Moneta. The idea of drawing a conceptual distinction between associational models and causal models is useful, we think, to improve on our modelling practices. However, in practice, the two are not so much separated.
The second lecture was part of Eleonora Montuschi‘s course Philosophy of the Social Sciences. She had asked me to give a presentation on causality. As usual, the big challenge is to give a sense of the beautiful complexity of the causality debate without scare people! I decided to focus on selected methodological and epistemological issues so that it would be a ‘natural’ follow up of the previous lecture.
The last day I took part in a workshop organised by Marcello Pelillo: Philosophical Aspects of Computer Science. We gather together to brainstorm about various ideas, approaches, questions, etc related to computer science and that we tackle in our own research. I have to admit that computer science does not exactly fall into my own area of expertise, and yet I discovered that I had a lot in common with them, and certainly many overlapping interests!
The epistemological relations between science and technology are a relatively under-explored topic. I started thinking about these issues a while ago, prompted by the practice of an emerging area of research: exposomics, or the science of exposure. (See e.g. this project.)
I presented some sketchy thoughts at SPT in Lisbon in 2013, and here is where my thinking on this issue is leading to. Still a lot of work to do before I can have some papers, but there we go.
The biennial meeting of the Philosophy of Science Association and the annual meeting of the History of Science Society took place in Chicago on 6-9 November 2014. Quite some interesting work has been presented there — let me just mention two sessions on the history of interdisciplinarity and on on the philosophy of interdisciplinarity (for the combined programme of the two meetings see here). I myself contributed to the second.
I presented a paper on the integration of social factors in the aetiology of diseases (especially non-communicable diseases). This is joint work with Mike Kelly and Rachel Kelly. Listen to this short interview of Mike and you’ll easily realise why it is important to reflect on what it means to have a mixed aetiology for disesases that are not ‘biologically communicable’ but are instead socially communicable (as it as been suggested in the Q&A after my talk). We argue in the paper that it is not sufficient to include socio-economic-behavioural factors in an epidemiological analysis, as mere ‘classificatory’ devices. These factors play an active role in aetiology and we need a concept of ‘mixed mechanism’ to account for that.
I had the chance to take part in the conference of the Danish Philosophical Association in Aalborg, early March 2014. For the occasion, I decided to try out again the idea of ‘variational reasoning’ in causal modelling. If you haven’t came across it already, it is actually quite simple. One question concerning causal reasoning is what notion(s) guide model building and model testing. This is an epistemological question. My answer is that we reason around variations. In other words, without variations we cannot detect causes at all. Of course, detecting variations is not enough to establish causal relations, and that’s why we need to impose further constraints, for instance regularity or invariance. If it is so simple, why is it so important? My view is that it sheds light on important aspects of causal epistemology, and it helps putting other causal notions (e.g., manipulation, regularity, …) in the right place of the #causalmosaic.
This is the second talk I gave in Brazil, in October 2013, in occasion of the V Seminar on Technology Management and Innovation in Health, where epidemiologist Mauricio Barreto organised a round table on Evaluation of Health Impact of Technological Interventions. There, I presented work on EBM and evidence hierarchies (output of a joint project with Phyllis Illari, Brendan Clarke, Donald Gillies, Jon Williamson).
By the way, if you are interested, we also set up a blog, called EBM+!
I was delighted to be invited to speak at the HPS department in Cambridge in November 2013. [I know it is a while ago, I’m catching up with stuff to put online.] The audience has been simply wonderful. Great questions, engaging and engaged, but not aggressive. Their questions helped me a lot rephrasing bits of the paper that stems from this (and some others!) presentations on the concept of invariance.
I hope it is now clear (or clearer) that the objective of this paper is not to criticise the notion of ‘invariance under intervention’ just for the sake of finding black spots in a given account. I’m trying to solve a genuine problem in social science methodology, and more generally in observational studies. Oh, the paper is now out in International Studies in Philosophy of Science, with the following title: What Invariance Is and How to Test for It. If you cannot access it, email me and I’ll gladly send you a copy.
Topoi, Volume 33, Issue 2, October 2014: Evidence and Causality in the Sciences
Guest editors: Phyllis Illari and Federica Russo
Most papers in this special issue have been presented at the conference ‘Evidence and Causality in the Sciences’ that took place at the University of Kent in September 2012. Right now the causality conference is running in Cologne, the theme this year is ‘Causality and Complexity in the Science’.