Searching for causality — statistics vs. history17 March, 2014 at 11:37 | Posted in Theory of Science & Methodology | 4 Comments
History and statistics serve a common purpose: to understand the causal force of some phenomenon. It seems to me, moreover, that statistics is a simplifying tool to understand causality, whereas history is a more elaborate tool. And by “more elaborate” I mean that history usually attempts to take into account both more variables as well as fundamentally different variables in our quest to understand causality.
To make this point clear, think about what a statistical model is: it is a representation of some dependent variable as a function of one or more independent variables, which we think, perhaps because of some theory, have a causal influence on the dependent variable in question. A historical analysis is a similar type of model. For example, a historian typically starts by acknowledging some development, say a war, and then attempts to describe, in words, the events that led to the particular development. Now, it is true that historians typically delve deeply into the details of the events predating the development – e.g., by examining written correspondence between officials, by reciting historical news clippings to understand the public mood, etc. – but this simply means that the historian is examining more variables than the simplifying statistician. If the statistician added more variables to his regression, he would be on his way to producing a historical analysis.
There is, however, one fundamental way in which the historian’s model is different from the statistician’s: namely, the statistician is limited by the fact that he can only consider precisely quantified variables in his model. The historian, in contrast, can add whatever variables he wants to his model. Indeed, the historian’s model is non-numeric …
It is my view that what differentiates whether history or statistics will be successful relates to the subject area to which each tool is applied. In subjects where precisely quantified variables are all we need to confidently determine the causal force of some phenomenon, statistics will be preferable; in subjects where imprecisely quantified variables play an important causal role, we need to rely on history.
It seems to me, moreover, that the line dividing the subjects to which we apply our historical or statistical tools cuts along the same seam as does the line dividing the social sciences from the natural sciences. In the latter, we can ignore imprecisely quantified variables, such as human beliefs, as these variables don’t play an important causal role in the movement of natural phenomena. In the former, such imprecisely quantified variables play a central role in the construction and the stability of the laws that govern society at any given moment.