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.

The E-value is a popular tool to assess the sensitivity to unmeasured confounding; the higher the E-value, the stronger the unmeasured confounding must be to “explain away” an observed association. However, despite its popularity, the E-value has also been heavily debated and criticized. Ioannidis et al (2019) argued that a high E-value may give an unwarrented optimistic impression, since the accumulated effect of many unmeasured confounders may be large and “trump” even a high E-value, even though the effect of each separate cofounder is small. In contrast, Greenland (2020) argued that a small E-value may give an unnecessarily pessimistic impression, since bias by unmeasured confounders may be weakened considerably due to their associations with measured confounders. These criticisms may appear contradictory, and leave the reader wondering whether E-values should be interpreted as being too optimistic or too pessimistic. In this presentation we will attempt to reconcile these criticisms, and use a real data example to argue that both interpretations are valid. The presentation will be largely non-technical, and focus on fundamental conceptual issues.

Foundational work on causal inference and dynamic treatment regimes presents a promising pathway towards precision medicine. In a precision-medicine system, decision rules might be algorithmically individualized based on an optimal rule previously learned from non-experimental or experimental data. However, there is some resistance to the notion that implementation of an optimal regime, successfully learned from the data, will result in better expected outcomes on average, compared to existing human-decision rules: care providers may be inclined to override the treatment recommendations provided by the identified optimal regimes, based on their privileged patient observations. In this talk, I will review existing methodology for learning optimal regimes and clarify the validity of the care provider's skepticism. Then, I will present methodology for leveraging human intuition by identifying a super-optimal regime using data generated by either nonexperimental or experimental studies, and clarify when a fusion of such data is beneficial. The superoptimal regime will indicate to a care provider -- in an algorithm-assisted decision setting -- precisely when expected outcomes would be maximized if the care provider would override the optimal regime recommendation and, importantly, when the optimal regime recommendation should be followed regardless of the care-provider's assessment.

In this talk, I will discuss different aspects of estimand choice in the presence of an intermediate (or mediating) variable of scientific interest. This will include an overview of the subtle differences between different flavours of direct and indirect effects already suggested in the mediation analysis literature. I will also discuss new perspectives on the role of heterogeneity, and its consequences for the transportability of mediation estimands (as well as total effect estimands) across different populations, in particular when there are causal effects in qualitatively opposite directions along different pathways from exposure to outcome.