Framing causal questions in life course epidemiology

TL;DR

This paper discusses how counterfactual thinking enhances the clarity and precision of causal questions in life course epidemiology, emphasizing the importance of proper framing and evaluation of evidence.

Contribution

It introduces principles of counterfactual thinking to improve the framing of causal questions and analysis in life course epidemiology, with practical examples using cohort data.

Findings

Counterfactual thinking clarifies causal effect definitions.

Proper framing improves evidence evaluation.

Examples demonstrate analysis of timing, mediation, and confounders.

Abstract

We describe the principles of counterfactual thinking in providing more precise definitions of causal effects and some of the implications of this work for the way in which causal questions in life course research are framed and evidence evaluated. Terminology is explained and examples of common life course analyses are discussed that focus on the timing of exposures, the mediation of their effects, observed and unobserved confounders, and measurement error. The examples are illustrated by analyses using singleton and twin cohort data.