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Preface to the papers on ‘Causal inference from non‐experimental studies: challenges, developments and applications’

This themed issue of the journal has come together in response to a call launched in 2019 that asked for contributions in novel causal inference methods motivated by substantive applications. As questions regarding causes and effects arise in a variety of settings, so do the contributions included here, which range from applications in clinical medicine (Andrews and his colleagues and Hunt and his colleagues), public health (Cuellar and Kennedy, Longford, and Samartsidis and his colleagues), health policy (Keele and his colleagues and Pimentel and his colleagues), epidemiology (Kim and his colleagues, Resa and Zubizarreta, von Cube and her colleagues and Zeng and his colleagues), biology (Castelletti and Consonni, and Foraita and her colleagues), economics (Briseño Sanchez and his colleagues and Saramago and his colleagues), law and criminology (Mauro and her colleagues, and Ogburn and her colleagues), politics (Imai and Jiang) and education (Corradi and Musio, Licari and Mattei, and Lockwood and McCaffrey). Although these applications are very varied, the challenges that they pose are recognizable across disciplines. Indeed the common thread across these papers is their attempt to draw causal conclusions from observational data, with limitations in data quality and potential biases encountered almost universally. These papers contribute to achieving this goal by adopting the formality of modern causal inference to formulate and address causal questions of interest and to deal with the challenges that are posed by data. They use concepts and language of counterfactual reasoning to define targets of estimation (e.g. the average causal effect and the local average causal effect) and to describe the relevant identifying assumptions (e.g. causal consistency, no interference and ignorability). This commonality of language, but not of applications, makes this issue so interesting: the methodological developments have different motivations but have the potential to extend causal inference across disciplines. We have grouped the contributions from these papers according to the methodological areas that they have extended, namely with regard to methods that