The field of ostracism research is witnessing a growing interest in understanding ostracism - being excluded and ignored - as a lived experience 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 draw on insights from the well-established causal inference framework to emphasize a critical step for strengthening causal rigor: stating the causal estimand. Using an intuitive example, we illustrate what a causal estimand is, how to define it, and why it matters. With this article, we encourage readers to think clearly about causal estimands before conducting any data analysis. This conceptual step holds the potential for enhancing the rigor and precision of research studying ostracism as a naturally occurring phenomenon.