Online Causal Inference Seminar

Tuesday, March 21, 2023 [Link to join] (ID: 996 2837 2037, Password: 386638)

• Speaker: Jessica Young (Harvard University)
• Title: Causal inference with competing events
• Discussant: Catherine Lesko (Johns Hopkins University), Q&A moderator: Mats Stensrud (EPFL)
• Abstract: A competing (risk) event is any event that makes it impossible for the event of interest in a study to occur. For example, cardiovascular disease death is a competing event for prostate cancer death because an individual cannot die of prostate cancer once he has died of cardiovascular disease. Various statistical estimands have been posed in the classical competing risks literature, most prominently the cause-specific cumulative incidence, the marginal cumulative incidence, the cause-specific hazard, and the subdistribution hazard. Here we will discuss the interpretation of counterfactual contrasts in each of these estimands under different treatments and consider possible limitations in their interpretation when a causal treatment effect on the event of interest is the goal and treatment may affect future event processes.  In turn, we argue that choosing a target causal effect in this setting fundamentally boils down to whether or not we choose to be satisfied estimating total effects, that capture all mechanisms by which treatment affects the event of interest, including via effects on competing events.  When we deem the total effect insufficient to answer our underlying question, we consider alternative targets of inference that capture treatment mechanism for competing event settings, with emphasis on the recently proposed separable effects.