# Hierarchical Capsule Prediction Network for Marketing Campaigns Effect

arXiv: https://arxiv.org/abs/2208.10113 · HTML: https://mlsanity.com/paper/2208.10113/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 22 Aug 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [Learning to Generate Long-term Future via Hierarchical Prediction](https://mlsanity.com/paper/1704.05831/) (2017)
- [Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data](https://mlsanity.com/paper/2106.02881/) (2021)
- [Matching in Selective and Balanced Representation Space for Treatment Effects Estimation](https://mlsanity.com/paper/2009.06828/) (2020)
- [Graph Capsule Convolutional Neural Networks](https://mlsanity.com/paper/1805.08090/) (2018)
- [Hierarchical Graph Capsule Network](https://mlsanity.com/paper/2012.08734/) (2020)
- [Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data](https://mlsanity.com/paper/2202.10885/) (2022)
- [Multi-Task Adversarial Learning for Treatment Effect Estimation in Basket Trials](https://mlsanity.com/paper/2203.05123/) (2022)

## Built on by (most cited first, 5 of 5)

- [Incorporating Causal Analysis into Diversified and Logical Response Generation](https://mlsanity.com/paper/2209.09482/) (2022)
- [Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting](https://mlsanity.com/paper/2407.19697/) (2024)
- [Multiple Treatments Causal Effects Estimation with Task Embeddings and Balanced Representation Learning](https://mlsanity.com/paper/2511.09814/) (2025)
- [Causal Interventional Prediction System for Robust and Explainable Effect Forecasting](https://mlsanity.com/paper/2407.19688/) (2024)
- [Barycentric Fused Gromov-Wasserstein Balancing for Causal Inference under Multiple Treatments](https://mlsanity.com/paper/2608.22024/) (2026)

## Abstract (arXiv)

Marketing campaigns are a set of strategic activities that can promote a business's goal. The effect prediction for marketing campaigns in a real industrial scenario is very complex and challenging due to the fact that prior knowledge is often learned from observation data, without any intervention for the marketing campaign. Furthermore, each subject is always under the interference of several marketing campaigns simultaneously. Therefore, we cannot easily parse and evaluate the effect of a single marketing campaign. To the best of our knowledge, there are currently no effective methodologies to solve such a problem, i.e., modeling an individual-level prediction task based on a hierarchical structure with multiple intertwined events. In this paper, we provide an in-depth analysis of the underlying parse tree-like structure involved in the effect prediction task and we further establish a Hierarchical Capsule Prediction Network (HapNet) for predicting the effects of marketing campaigns. Extensive results based on both the synthetic data and real data demonstrate the superiority of our model over the state-of-the-art methods and show remarkable practicability in real industrial applications.