# Optimising Individual-Treatment-Effect Using Bandits

arXiv: https://arxiv.org/abs/1910.07265 · HTML: https://mlsanity.com/paper/1910.07265/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [To do or not to do: cost-sensitive causal decision-making](https://mlsanity.com/paper/2101.01407/) (2021)
- [Uplifting Bandits](https://mlsanity.com/paper/2206.04091/) (2022)

## Abstract (arXiv)

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.