Strategic interactions among rational, self-interested actors are commonly theorized in the behavioral, economic, and social sciences. The theorized strategic processes have traditionally been modeled with multi-stage structural estimators, which improve parameter estimates at one stage by using the information from other stages. Multi-stage approaches, however, impose rather strict demands on data availability: data must be available for the actions of each strategic actor at every stage of the interaction. Observational data are not always structured in a manner that is conducive to these approaches. Moreover, the theorized strategic process implies that these data are missing not at random. In this paper, I derive a strategic logistic regression model with partial observability that probabilistically estimates unobserved actor choices related to earlier stages of strategic interactions. I compare the estimator to traditional logit and split-population logit estimators using Monte Carlo simulations and a substantive example of the strategic firm-regulator interaction associated with pollution and environmental sanctions.