Preface

Causal inference forms the central pillar of many scientific queries, with a wide range of successful applications. Despite a growing body of literature dedicated to unraveling causal inference, with popular use in medicine and epidemiology, the temporal intricacies embedded in survival data usher forth distinctive methodological hurdles that are not readily resolved through conventional methods for uncensored data. Several of these challenges found early elucidation in the pioneering work of James Robins, which was kindled by inquiries into AIDS epidemiology. However, the landscape remains somewhat barren when it comes to instructional resources and tutorials tailored explicitly for causal inference applied to survival data. Moreover, the emergence of novel models and methodologies within the domain of causal inference also brings new challenges when dealing with survival data. With great pleasure, we present to you this special issue of lifetime data analysis, curated to showcase a wide diversity of recent advances in both theory and application within the realm of causal inference as it intertwines with survival analysis. Within this volume, we have compiled the following nine manuscripts. …