Digital travel platforms often operate multiple marketing journeys simultaneously, resulting in overlapping user exposures that bias the standard A/B lift estimation. Because traditional lift experiments assume treatment isolation, the observed lifts reflect only marginal effects and may substantially underestimate the total incremental impact of each journey. This work introduces a Hierarchical Causal Lift Model that decomposes pure and global effects under journey overlap. Each journey is modeled as a multiplicative causal factor, and the interaction terms capture potential synergies or cannibalizations. The model is estimated through a Monte Carlo framework that incorporates uncertainty in overlap proportions, observed lifts, and single-journey effects. Regularized non-linear least squares are complemented with Monte Carlo simulation to quantify parameter uncertainty and assess the robustness of the solution. Applied to an active user base of approximately three million users, the model reveals positive but modest synergies between journeys and shows that pure lifts are significantly larger than those observed experimentally. The predicted global lift closely matches the experimentally measured value, demonstrating the ability of the model to recover incremental effects in an interpretable manner.

翻译：数字旅游平台通常同时运行多个营销旅程，导致用户曝光重叠，从而对标准A/B提升估计产生偏差。由于传统提升实验假设处理隔离，观测到的提升仅反映边际效应，可能严重低估每个旅程的总增量影响。本文提出一种层次化因果提拉模型，在旅程重叠条件下分解纯效应与全局效应。每个旅程被建模为乘法因果因子，交互项捕捉潜在的协同或蚕食效应。该模型通过蒙特卡洛框架进行估计，该框架纳入重叠比例、观测提升及单旅程效应的不确定性。正则化非线性最小二乘法与蒙特卡洛模拟相结合，用于量化参数不确定性并评估解的鲁棒性。将该模型应用于约三百万活跃用户时，它揭示了旅程间积极但适度的协同效应，并表明纯提升显著大于实验观测值。预测的全局提升与实验测量值高度吻合，证明了模型以可解释方式恢复增量效应的能力。