Treatment effect optimisation in dynamic environments

Applying causal methods to fields such as healthcare, marketing, and economics receives increasing interest. In particular, optimising the individual-treatment-effect - often referred to as uplift modelling - has peaked in areas such as precision medicine and targeted advertising. While existing techniques have proven useful in many settings, they suffer vividly in a dynamic environment. To address this issue, we propose a novel optimisation target that is easily incorporated in bandit algorithms. Incorporating this target creates a causal model which we name an uplifted contextual multi-armed bandit. Experiments on real and simulated data show the proposed method to effectively improve upon the state-of-the-art. All our code is made available online at https://github.com/vub-dl/u-cmab.

Automated summary

Applying causal methods in fields like healthcare, marketing, and economics is gaining increasing interest. The research on individual-treatment-effect optimization, also known as uplift modelling, has reached its peak in precision medicine and targeted advertising. Existing techniques have shown their utility in many scenarios, but they suffer from limitations in dynamic environments. To address this, the researchers propose a novel optimization target called uplifted contextual multi-armed bandit, which effectively improves upon the state-of-the-art according to experiments on real and simulated data.