Abstract
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.
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 language | English |
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Title of host publication | Recent Advances in Artificial Intelligence (RAAI) 2019 |
Place of Publication | Bucharest, Romania |
Publisher | University of Bucharest |
Pages | 1-3 |
Number of pages | 3 |
Publication status | Published - 2019 |
Event | Recent Advances in Artificial Intelligence - University of Bucharest, Bucharest, Romania Duration: 28 Jun 2019 → 30 Jun 2019 Conference number: 3 https://conferences.unibuc.ro/raai2019/ |
Conference
Conference | Recent Advances in Artificial Intelligence |
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Abbreviated title | RAAI |
Country/Territory | Romania |
City | Bucharest |
Period | 28/06/19 → 30/06/19 |
Internet address |