Confounding is a major issue in observational epidemiological studies. This paper describes two postestimation commands for assessing confounding effects. One command (confall) displays and plots all possible effect estimates against one of p-value, Akaike information criterion, or Bayesian information criterion. This computing-intensive procedure allows researchers to inspect the variability of the effect estimates from various possible models. Another command (chest) uses a stepwise approach to identify variables that have substantially changed the effect estimate. Both commands can be used after most common estimation commands in epidemiological studies, such as logistic regression, conditional logistic regression, Poisson regression, linear regression, and Cox proportional hazards models.