Abstract: This paper proposes a simple technique for assessing the range of plausible causal con- clusions from observational studies with a binary outcome and an observed categorical covariate. The technique assesses the sensitivity of conclusions to assumptions about an unobserved binary covariate relevant to both treatment assignment and response. A medical study of coronary artery disease is used to illustrate the technique. Inevitably, the results of clinical studies are subject to dispute. In observational studies, one basis for dispute is obvious: since patients were not assigned to treatments at random, patients at greater risk may be over-represented in some treatment groups. This paper proposes a method for assess- ing the sensitivity of causal conclusions to an unmeasured patient characteristic relevant to both treatment assignment and response. Despite their limitations, observational studies will continue to be a valuable source of information, and therefore it is prudent to develop appropriate methods of analysis for them. Our sensitivity analysis consists of the estimation of the average effect of a treatment on a binary outcome variable after adjustment for observed categorical covariates and an unobserved binary covariate u, under several sets of assumptions about u. Both Cornfield et al. (1959) and Bross (1966) have proposed guidelines for determining whether an unmeasured binary covariate having specified properties could explain all of the apparent effect of a treatment, that is, whether the treatment effect, after adjustment for u could be zero. Our method has two advantages: first, Cornfield et al. (1959) and Bross (1966) adjust only for the unmeasured binary covariate u, whereas we adjust for measured covariates in addition to the unmeasured covariate u. Second, Cornfield et al. (1959) and Bross (1966, 1967) only judge whether the effect of the treatment could be zero having adjusted for u, where Cornfield et al. (1959) employ an implicit yet extreme assumption about u. In contrast, we provide actual estimates of the treatment effect adjusted for both u and the observed categorical covariates under any assumption about u. In principle, the ith of the N patients under study has both a binary response r1i that would have resulted if he had received the new treatment, and a binary response ro0 that would have resulted if he had received the control treatment. In this formulation, treatment effects are comparisons of r1i and roi, such as r1i - roi. Since each patient receives only one treatment, either rli or ro0 is observed, but not both, and therefore comparisons of rli and roi imply some degree of speculation. Treatment effects defined as comparisons of the two potential responses, r1i and roi, of individual patients are implicit in Fisher's (1953) randomization test of the sharp null