Causal models and causal modelling in obesity: foundations, methods and evidence.

Discussing causes in science, if we are to do so in a way that is sensible, begins at the root. All too often, we jump to discussing specific postulated causes but do not first consider what we mean by, for example, causes of obesity or how we discern whether something is a cause. In this paper, we address what we mean by a cause, discuss what might and might not constitute a reasonable causal model in the abstract, speculate about what the causal structure of obesity might be like overall and the types of things we should be looking for, and finally, delve into methods for evaluating postulated causes and estimating causal effects. We offer the view that different meanings of the concept of causal factors in obesity research are regularly being conflated, leading to confusion, unclear thinking and sometimes nonsense. We emphasize the idea of different kinds of studies for evaluating various aspects of causal effects and discuss experimental methods, assumptions and evaluations. We use analogies from other areas of research to express the plausibility that only inelegant solutions will be truly informative. Finally, we offer comments on some specific postulated causal factors. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.

Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling.

The estimation of parameters and model structure for informing infectious disease response has become a focal point of the recent pandemic. However, it has also highlighted a plethora of challenges remaining in the fast and robust extraction of information using data and models to help inform policy. In this paper, we identify and discuss four broad challenges in the estimation paradigm relating to infectious disease modelling, namely the Uncertainty Quantification framework, data challenges in estimation, model-based inference and prediction, and expert judgement. We also postulate priorities in estimation methodology to facilitate preparation for future pandemics.

A comment on the PCAST report: Skip the "match"/"non-match" stage.

This letter comments on the report "Forensic science in criminal courts: Ensuring scientific validity of feature-comparison methods" recently released by the President's Council of Advisors on Science and Technology (PCAST). The report advocates a procedure for evaluation of forensic evidence that is a two-stage procedure in which the first stage is "match"/"non-match" and the second stage is empirical assessment of sensitivity (correct acceptance) and false alarm (false acceptance) rates. Almost always, quantitative data from feature-comparison methods are continuously-valued and have within-source variability. We explain why a two-stage procedure is not appropriate for this type of data, and recommend use of statistical procedures which are appropriate.

"Imagine a can opener"--the magic of principal stratum analysis.

Abstract We extend Pearl's criticisms of principal stratification analysis as a method for interpreting and adjusting for intermediate variables in a causal analysis. We argue that this can be meaningful only in those rare cases that involve strong functional dependence, and even then may not be appropriate.

Statistics on trial.

Although the concerns of Statistics and the Law might seem to have little to do with one other, they do share some fundamental common interests, such as interpretation of evidence, hypothesis testing and decision-making under uncertainty. Philip Dawid takes up questions that, though long of interest to a few scholars, have only recently become the focus of much attention.