Plausibility and the Benefit of Theoretical Reasoning: Comment on Austin Bradford Hill (1965)

Observational Studies 6 (2020) 20-23 Submitted 10/19; Published 1/20 Plausibility and the Benefit of Theoretical Reasoning: Comment on Austin Bradford Hill (1965) A. James O’Malley James.OMalley@dartmouth.edu Department of Biomedical Data Science and The Dartmouth Institute of Health Policy and Clinical Practice Geisel School of Medicine, Dartmouth University Lebanon, NH 03756, USA I greatly enjoyed reading the paper “The Environment and Disease: Association or Causation ” by Professor Austin Bradford Hill (ABH). The paper continues to be held in high regard and is massively cited (9,359 citations as of October 20, 2019). The paper is centered around nine aspects of statistical association that ABH suggests be considered before deciding if causation should be claimed. In some fields, these points have erroneously been viewed and taught as causal criteria (Phillips and Goodman 2004). Given that “Association or Causation” discernment is receiving increasing attention in the statistical literature, and that we’re in the midst of the emergence of data science and ever-growing volumes of observational data, reviewing ABH (1965) and assessing whether there are insights that can inform contemporary statistical analysis is timely and valuable. ABH (1965) has been the subject of much review and discussion over many years (e.g., Philips and Goodman 2004; Thygesen, Andersen and Andersen 2005; Fedak et al 2015). Often reviews have systematically considered each of the nine points and offered discussion and critique about each. Because the appropriateness or not of these points has already received an extensive amount of attention, I have focused my comment on a specific topic that while being inherent to ABH (1965) has not been directly addressed. I focus on the role of theoretical models – often used in economics and sociology to generate causal stories and allied hypotheses prior to analyzing data or even designing a study – in relation to the task of distinguishing cause from association. Is the ability to construct and exploit theoretical models in order to devise ways of testing their legitimacy an under-appreciated skillset that ought to be part of statistical (and data science) practice and education? In the following, I first provide some general comments and then give an illustrative example. The point in ABH (1965) that is most directly relevant to my comment is Plausibility, one of the least considered of the nine points. ABH states “It will be helpful if the causation we suspect is biologically plausible.” I agree with this statement and think that it can be extended to “It will be helpful if the causation we suspect is plausible in the scientific or other setting being analyzed.” In other words, does the causal story make sense given current knowledge. In order to answer this question, it is imperative that one understand the underlying science or subject area knowledge. If the causal story is plausible then the skill of being able to think theoretically or conceptually about it may allow tests of hypotheses and ways of identifying parameters in the theoretical model to be constructed that data can subsequently adjudicate. c 2020 A. James O’Malley. Plausibility and Benefit of Theoretical Reasoning Since 1965, the presence and importance of statistics in science and medicine has grown. However, in many settings, roles of statisticians have been restricted to data analysis as opposed to the art of linking the broader scientific perspective underlying research or other investigation to data. To prepare students for such data-focused careers, the budding statistician is typically supplied with practice problems to solve. This tailor-made approach bypasses a lot of the artfulness of being able to reason theoretically in the application-side of a problem to identify the key aspects of it that can be tested by data. Thus, even if their empirical work is performed with much creativity and skill, in terms of overall impact the role of a traditionally-trained statistician may not be as great as it could be. The statistician is often a “Devil’s advocate”. While being a Devil’s advocate is certainly needed, if the ultimate goal is to get the best answer possible using current resources, the skill of being able to discern what should be tested in the first place is vital...