This paper introduces new statistical models, Boolean logit and probit, that allow researchers to model binary outcomes as the results of Boolean interactions among independent causal processes. Each process (or “causal path”) is modeled as the unobserved outcome in a standard logit or probit equation, and the dependent variable is modeled as the observed product of their Boolean interaction. Up to five causal paths can be modeled, in any combination—A and B and C produce Y, A and (B or [C and D]) produce Y, etc.