Causal assessment in demographic research

Abstract

Abstract Causation underlies both research and policy interventions. Causal inference in demography is however far from easy, and few causal claims are probably sustainable in this field. This paper targets the assessment of causality in demographic research. It aims to give an overview of the methodology of causal research, pointing out various problems that can occur in practice. The “Intervention studies” section critically examines the so-called gold standard in causality assessment in experimental studies, randomized controlled trials, and the use of quasi-experiments and interventions in observational studies. The “Multivariate statistical models” section deals with multivariate statistical models linking a mortality or fertility indicator to a series of possible causes and controls. Single and multiple equation models are considered. The “Mechanisms and structural causal modelling” section takes into account a more recent trend, i.e., mechanistic explanations in causal research, and develops a structural causal modelling framework stemming from the pioneering work of the Cowles Commission in econometrics and of Sewall Wright in population genetics. The “Assessing causality in demographic research” section examines how causal analysis could be further applied in demographic studies, and a series of proposals are discussed for this purpose. The paper ends with a conclusion pointing out, in particular, the relevance of structural equation models, of triangulation, and of systematic reviews for causal assessment.

Keywords

• Causality
• Structural modelling
• Randomized trials
• Counterfactuals
• Multivariate statistical models
• Causal graphs