Optimising Individual-Treatment-Effect Using Bandits
This addresses the challenge of maintaining effective uplift modeling in dynamic settings such as marketing and healthcare, though it appears incremental by combining existing bandit techniques with uplift modeling.
The paper tackles the problem of optimizing individual treatment effects in dynamic environments like marketing, where concept drift can degrade performance, by proposing the U-CMAB approach, which shows favorable results compared to state-of-the-art methods in experiments on real and simulated data.
Applying causal inference models in areas such as economics, healthcare and marketing receives great interest from the machine learning community. In particular, estimating the individual-treatment-effect (ITE) in settings such as precision medicine and targeted advertising has peaked in application. Optimising this ITE under the strong-ignorability-assumption -- meaning all confounders expressing influence on the outcome of a treatment are registered in the data -- is often referred to as uplift modeling (UM). While these techniques have proven useful in many settings, they suffer vividly in a dynamic environment due to concept drift. Take for example the negative influence on a marketing campaign when a competitor product is released. To counter this, we propose the uplifted contextual multi-armed bandit (U-CMAB), a novel approach to optimise the ITE by drawing upon bandit literature. Experiments on real and simulated data indicate that our proposed approach compares favourably against the state-of-the-art. All our code can be found online at https://github.com/vub-dl/u-cmab.