A general framework for causal classification


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.

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This work has been supported by ARC Discovery Projects Grant DP170101306, ARC Discovery Early Career Researcher Award DE200100200, and National Science Foundation of China (under grant 61876206).

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Li, J., Zhang, W., Liu, L. et al. A general framework for causal classification. Int J Data Sci Anal 11, 127–139 (2021). https://doi.org/10.1007/s41060-021-00249-1

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  • Causal effect estimation
  • Causal heterogeneity
  • Uplift modelling