Causal Inference from Non-Experimental Studies: Challenges, Developments and Applications
Decisions in many fields—including medicine, public health, social science, and finance— depend critically on the appropriate evaluation of causal effects of competing treatments, exposures and/or policies. Formal causal inference is crucial to assess these effects, whether the data come from experimental or observational studies, or a combination of both.
There have been numerous methodological and applied contributions to the causal inference literature especially over the last two decades. They have arisen from different fields, including statistics, epidemiology, public health, econometrics, and demography, and have impacted not only on the work carried out in academia but also on real-world decision-making.
More recently, newer challenges to the field have emerged, in particular from novel study designs, large and messy data sources, and complex treatment assignment mechanisms. This themed issue of the Journal of the Royal Statistical Society, Series A, is dedicated to these newer challenges, associated developments and applications. As usual with Series A, the focus is on the development and/or evaluation of innovative methodology that is directly motivated by, and substantially increases our understanding of, real data from non-randomised exposures and interventions in social and medical settings.
The deadline for manuscript submissions is midnight on 30 September 2018. Submissions, which should clearly indicate JRSS-A Causal Inference Issue in the cover letter, should be made in the usual way, online at https://mc.manuscriptcentral.com/jrss, where further guidance about the structure, length and format of manuscripts may be found. All manuscripts will be peer reviewed in line with the journal’s standard policy. However, in order to produce the themed issue in a timely manner, authors will be asked to complete revisions within eight weeks of receiving referee reports.