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2024 | OriginalPaper | Buchkapitel

Treatment Effect Estimation Under Unknown Interference

verfasst von : Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Hisashi Kashima

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer Nature Singapore

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Abstract

Causal inference is a powerful tool for effective decision-making in various areas, such as medicine and commerce. For example, it allows businesses to determine whether an advertisement has a role in influencing a customer to buy the advertised product. The influence of an advertisement on a particular customer is considered the advertisement’s individual treatment effect (ITE). This study estimates ITE from data in which units are potentially connected. In this case, the outcome for a unit can be influenced by treatments to other units, resulting in inaccurate ITE estimation, a phenomenon known as interference. Existing methods for ITE estimation that address interference rely on knowledge of connections between units. However, these methods are not applicable when this connection information is missing due to privacy concerns, a scenario known as unknown interference. To overcome this limitation, this study proposes a method that designs a graph structure learner, which infers the structure of interference by imposing an \(L_0\)-norm regularization on the number of potential connections. The inferred structure is then fed into a graph convolution network to model interference received by units. We carry out extensive experiments on several datasets to verify the effectiveness of the proposed method in addressing unknown interference.

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Note that we assume the edges of the interference graph to be directed.

As the information of unit i itself is always important for computing the level of interference, we set \(\hat{z}_{ii}\) to 1.

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Titel: Treatment Effect Estimation Under Unknown Interference
verfasst von: Xiaofeng Lin
Guoxi Zhang
Xiaotian Lu
Hisashi Kashima
Verlag: Springer Nature Singapore
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-981-9722-52-5

Electronic ISBN: 978-981-9722-53-2

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-2253-2_3

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