Investigating lived ostracism: Valid causal inference requires articulating the causal estimand

Our field is witnessing a surging interest in understanding lived experiences of ostracism outside of the laboratory context. How do researchers draw valid causal conclusions about naturally occurring experiences of ostracism without relying on experimental designs? In this article, we put forth that a critical first step is to clearly state the causal question and the causal quantity researchers aim to estimate (known as the causal estimand). This step should precede any data analysis. Using an intuitive example, we illustrate why the default causal estimand (average treatment effect) does not necessarily align with the causal effect of substantive interest pertaining to lived ostracism. We further review a selection of causal estimands which are more suited to answer causal queries of lived ostracism. We believe that selecting a causal estimand carefully tailored to the research question enhances the rigor and precision of research studying ostracism as a naturally occurring phenomenon.