Search results

What kind of epidemiological modeling works well, and what kind doesn't? This is determined by the nature of the target: the relevant causal relations are unstable across contexts, which tells against any modeling that assumes otherwise. I look at two influential examples from the Covid pandemic. The first example is the paper from Imperial College, London, that projected future infection rates under various policy scenarios, and that in March 2020 was influential in persuading the UK government to impose a lockdown (Ferguson et al 2020). Because it assumes stability, this first example of modeling fails: it carries no epistemic force at all. A different modeling strategy is required, one that is less ambitious but more effective. This is illustrated by the second example: the paper, also from Imperial College, London, that in December 2020 first estimated the transmissibility of the Alpha variant (Volz et al 2020). This second, contextual example of modeling works well.

Has the rise of data-intensive science, or 'big data', revolutionized our ability to predict? Does it imply a new priority for prediction over causal understanding, and a diminished role for theory and human experts? I examine four important cases where prediction is desirable: political elections, the weather, GDP, and the results of interventions suggested by economic experiments. These cases suggest caution. Although big data methods are indeed very useful sometimes, in this paper's cases they improve predictions either limitedly or not at all, and their prospects of doing so in the future are limited too.

Weighted explanations , whereby some causes are deemed more important than others, are ubiquitous in historical studies. Drawing from influential recent work on causation, I develop a definition of causal-explanatory strength. This makes clear exactly which aspects of explanatory weighting are subjective and which objective. It also sheds new light on several traditional issues, showing for instance that: underlying causes need not be more important than proximate ones; several different causes can each be responsible for most of an effect; small causes need not be less important than big ones; and non-additive interactive effects between causes present no particular difficulty.