Do CRM Systems Cause One-to-One Marketing Effectiveness?

Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology

Propensity score methods were proposed by Rosenbaum and Rubin [Biometrika 70 (1983) 41--55] as central tools to help assess the causal effects of interventions. Since their introduction more than two decades ago, they have found wide application in a variety of areas, including medical research, economics, epidemiology and education, especially in those situations where randomized experiments are either difficult to perform, or raise ethical questions, or would require extensive delays before answers could be obtained. In the past few years, the number of published applications using propensit…

Adapting Neural Networks for Uplift Models

Uplift is a particular case of individual treatment effect modeling. Such models deal with cause-and-effect inference for a specific factor, such as a marketing intervention. In practice, these models are built on customer data who purchased products or services to improve product marketing. Uplift is estimated using either i) conditional mean regression or ii) transformed outcome regression. Most existing approaches are adaptations of classification and regression trees for the uplift case. However, in practice, these conventional approaches are prone to overfitting. Here we propose a new met…

数据集变化流下连续因果效应的估计

The Estimation of Continual Causal Effect for Dataset Shifting Streams

本文提出ICE-PKD框架，通过多处理uplift network和增量训练策略应对时间性数据集变化，提升在线营销中的因果效应估计性能，并已部署于网约车平台。

Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions

For many kinds of interventions, such as a new advertisement, marketing intervention, or feature recommendation, it is important to target a specific subset of people for maximizing its benefits at minimum cost or potential harm. However, a key challenge is that no data is available about the effect of such a prospective intervention since it has not been deployed yet. In this work, we propose a split-treatment analysis that ranks the individuals most likely to be positively affected by a prospective intervention using past observational data. Unlike standard causal inference methods, the spli…

持续因果效应估计：挑战与机遇

Continual Causal Effect Estimation: Challenges and Opportunities

本文定义了持续处理效应估计问题，指出大数据时代观测数据因果推断在可扩展性、适应性和可访问性上的新挑战，并提出可能的解决方案与未来研究方向。

识别模型质量对用户参与度的影响：一种结合合成数据验证的版本内因果估计器

Identifying Model Quality Effects on User Engagement: A Within-Version Causal Estimator with Synthetic Data Validation

针对LLM模型更新与用户参与度的因果识别难题，提出结合合成数据验证的版本内因果估计器，经衰减调整后可准确估计模型质量对参与度的影响。