Authors: Shengjia Cui; Xianglong Qi; Xiao Wang; Chen Zeng
Addresses: Baidu Co., Ltd., Beijing, China ' Liaoning Huading Technology Co., Ltd., Shenyang, Liaoning, China ' Baidu Co., Ltd., Beijing, China ' Baidu Co., Ltd., Beijing, China
Abstract: Determining the long-term causal effects as well as making decisions in a timely fashion are the most challenging and significant tasks in A/B tests. The key challenge is that long-term and short-term effects may be different, especially in an online controlled experiment that a low-qualified ads strategy with high short-term revenue may hurt users' service experience in the future. We propose a 'Transformer for Long-Term Effect (TLTE)' method to encode the short-term latent features and predict the outcome of the long-term to estimate the long-term effects. TLTE utilises the transformer structure to capture the reasoning feature, considering the global information by the self-attention mechanism. Extensive experiments are conducted on public day-level data sets and our collected large-scale hourly data set, providing the flexible and sensitive analysis. Both quantitative and qualitative experiments demonstrate that our model has the ability to meet requirements of real-world scenarios and improves accuracy of long-term effect estimation.
Keywords: long term; transformer; causal effect.
International Journal of Computer Applications in Technology, 2022 Vol.69 No.4, pp.322 - 333
Received: 13 Nov 2021
Accepted: 03 Dec 2021
Published online: 07 Mar 2023 *