March 29, 2024, 4:42 a.m. | George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros, Jun Pang

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.19289v1 Announce Type: new
Abstract: Estimating causal effects in e-commerce tends to involve costly treatment assignments which can be impractical in large-scale settings. Leveraging machine learning to predict such treatment effects without actual intervention is a standard practice to diminish the risk. However, existing methods for treatment effect prediction tend to rely on training sets of substantial size, which are built from real experiments and are thus inherently risky to create. In this work we propose a graph neural network …

abstract arxiv causal commerce cs.ai cs.lg e-commerce effects graph graph neural networks however machine machine learning networks neural networks practice prediction risk scale standard stat.me treatment type

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