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Tag: causal effects

What are the effects of COVID-19 on mortality? Individual-level causes of death and population-level estimates of casual impact

Published by demetriodor on April 27, 2020

Introduction How many people have died from COVID-19? What is the impact of COVID-19 on mortality in a population? Can we use excess mortality to estimate the effects of COVID-19? In this text I will explain why the answer to the first two questions need not be the same. That is, the sum of cases where COVID-19 has been determined to be the direct[1] cause of death need not be the same as the population-level estimate about the causal impact of COVID-19. When measurement of the individual-level causes of death is imperfect, using excess mortality (observed minus expected) to measure the impact of COVID-19 leads to an underestimate of the number of individual cases where COVID-19 has been the direct cause of death. Assumptions The major assumption on which the argument rests is that some of the people who have died from COVID-19 would have died from other causes, within a specified relatively short time-frame (say, within the month). It seems very reasonable to assume that at least some of the victims of COVID-19 would have succumbed to other causes of death. This is especially easy to imagine given that COVID-19 kills disproportionally the very old and that the ultimate causes of death that it provokes – respiratory problems, lungs failure, etc. – are shared with other common diseases with high mortality among the older population, such as the flu. Defining individual and population-level causal effects With this crucial assumption in mind, we can construct the following simple table. Cell…

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Explanation and the quest for ‘significant’ relationships. Part I

Published by demetriodor on February 17, 2012

The ultimate goal of social science is causal explanation*. The actual goal of most academic research is to discover significant relationships between variables. The two goals are supposed to be strongly related – by discovering (the) significant effects of exogenous (independent) variables, one accounts for the outcome of interest. In fact, the working assumption of the empiricist paradigm of social science research is that the two goals are essentially the same – explanation is the sum of the significant effects that we have discovered. Just look at what all the academic articles with ‘explanation’, ‘determinants’, and ’causes’ in their titles do – they report significant effects, or associations, between variables. The problem is that explanation and collecting significant associations are not the same. Of course they are not. The point is obvious to all uninitiated into the quantitative empiricist tradition of doing research, but seems to be lost to many of its practitioners. We could have discovered a significant determinant of X, and still be miles (or even light-years) away from a convincing explanation of why and when X occurs. This is not because of the difficulties of causal identification – we could have satisfied all conditions for causal inference from observational data, but the problem still stays. And it would not go away after we pay attention (as we should) to the fact that statistical significance is not the same as practical significance. Even the discovery of convincingly-identified causal effects, large enough to be of practical rather than only statistical significance, does not amount to explanation. A successful explanation needs to account for…

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