Mutual information is an attractive statistic for many applications because it completely captures the dependence between two random variables or vectors. Conditional mutual information (CMI) is particularly useful in settings, e.g. causal discovery, where it is necessary to quantify dependence between a pair of variables that may be mediated by other variables. CMI’s usage is rare in fields such as epidemiology, public policy, and social sciences due to its inability to handle mixtures of continuous and discrete random variables. While progress has been made on estimating mutual information for discrete and continuous variables, a CMI estimation method does not currently exist. This paper builds on prior research to develop a novel method for non-parametric CMI estimation for discrete and/or continuous variables. For each point, the method locally estimates CMI using its nearest neighbors then averages all local estimates. If a point’s nearest neighbor occupies that same location, the method recognizes that the point is likely discrete and alters the counting process. We prove that this estimator is consistent theoretically and demonstrate its performance empirically as well.