  In many applications, there is a need to predict the effect of an
intervention on different individuals from data. For example, which customers
are persuadable by a product promotion? which patients should be treated with a
certain type of treatment? These are typical causal questions involving the
effect or the change in outcomes made by an intervention. The questions cannot
be answered with traditional classification methods as they only use
associations to predict outcomes. For personalised marketing, these questions
are often answered with uplift modelling. The objective of uplift modelling is
to estimate causal effect, but its literature does not discuss when the uplift
represents causal effect. Causal heterogeneity modelling can solve the problem,
but its assumption of unconfoundedness is untestable in data. So practitioners
need guidelines in their applications when using the methods. In this paper, we
use causal classification for a set of personalised decision making problems,
and differentiate it from classification. We discuss the conditions when causal
classification can be resolved by uplift (and causal heterogeneity) modelling
methods. We also propose a general framework for causal classification, by
using off-the-shelf supervised methods for flexible implementations.
Experiments have shown two instantiations of the framework work for causal
classification and for uplift (causal heterogeneity) modelling, and are
competitive with the other uplift (causal heterogeneity) modelling methods.
