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 application. Optimising the ITE under the strong ignoreability 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 solve the UM problem by drawing upon bandit literature. Simulations indicate that our proposed approach significantly outperforms the state-of-the-art. As this research is still ongoing, we present only preliminary results in this extended abstract.
Original languageEnglish
Title of host publicationRecent Advances in Artificial Intelligence (RAAI) 2019
Place of PublicationBucharest, Romania
PublisherUniversity of Bucharest
Number of pages3
Publication statusPublished - 2019
EventRecent Advances in Artificial Intelligence - University of Bucharest, Bucharest, Romania
Duration: 28 Jun 201930 Jun 2019
Conference number: 3


ConferenceRecent Advances in Artificial Intelligence
Abbreviated titleRAAI
Internet address

ID: 47561389