A fundamental causal modelling task is to predict the effect of an intervention (or treatment) on an outcome, given context/covariates. Examples include predicting the effect of a medical treatment on patient health given patient symptoms and demographic information, or predicting the effect of ticket pricing on airline sales given seasonal fluctuations in demand. The problem becomes especially challenging when the treatment and context are complex (for instance, “treatment” might be a web ad design or a radiotherapy plan), and when only observational data is available (i.e., we have access to historical data, but cannot intervene ourselves). I will give an overview of some practical kernel methods for estimating causal effects of complex, high dimensional treatments from observational data. The approach is based on kernel conditional feature means, which represent conditional expectations of relevant model features. The methods will be applied to modelling employment outcomes for the US Job Corps program for Disadvantaged Youth.