Treatment effect optimisation in dynamic environments

Jeroen Berrevoets, Sam Verboven, Wouter Verbeke

Onderzoeksoutput: Articlepeer review


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
Originele taal-2English
Pagina's (van-tot)106-122
Aantal pagina's17
TijdschriftJournal of Causal Inference
Nummer van het tijdschrift1
StatusPublished - 31 mei 2022

Bibliografische nota

Funding Information:
Funding information : JB is funded by the W.D. Armstrong Trust Fund.

Publisher Copyright:
© 2022 Jeroen Berrevoets et al.

Copyright 2022 Elsevier B.V., All rights reserved.

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