Treatment effect optimisation in dynamic environments

Berrevoets Jeroen (), Verboven Sam () and Verbeke Wouter ()
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Berrevoets Jeroen: Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
Verboven Sam: Data Analytics Laboratory, Solvay Business School, Vrije Universiteit Brussel, Brussels, Belgium
Verbeke Wouter: Faculty of Economics and Business, Leuven.AI, KU Leuven, Leuven, Belgium

Journal of Causal Inference, 2022, vol. 10, issue 1, 106-122

Abstract: 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.

Keywords: bandit algorithms; uplift modelling; individual treatment effect (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:10:y:2022:i:1:p:106-122:n:3

DOI: 10.1515/jci-2020-0009

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