Hierarchical Causal Uplift Modeling in Overlapping Customer Journeys

TL;DR

This paper presents a hierarchical causal lift model for overlapping customer journeys on digital travel platforms, accurately estimating incremental effects and synergies overlooked by traditional methods.

Contribution

It introduces a novel hierarchical causal lift model that decomposes effects in overlapping journeys, incorporating uncertainty and synergy effects.

Findings

Pure lifts are significantly larger than observed experimental lifts.

The model reveals positive but modest synergies between journeys.

Predicted global lift closely matches experimental measurements.

Abstract

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.