# Tree-like Pairwise Interaction Networks

arXiv: https://arxiv.org/abs/2508.15678 · HTML: https://mlsanity.com/paper/2508.15678/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 21 Aug 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models](https://mlsanity.com/paper/1612.08468/) (2016)
- [Detecting Statistical Interactions from Neural Network Weights](https://mlsanity.com/paper/1705.04977/) (2017)
- [Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction](https://mlsanity.com/paper/1910.05552/) (2019)
- [Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection](https://mlsanity.com/paper/2209.09326/) (2022)
- [Enhancing Actuarial Non-Life Pricing Models via Transformers](https://mlsanity.com/paper/2311.07597/) (2023)
- [The Credibility Transformer](https://mlsanity.com/paper/2409.16653/) (2024)
- [Detection of Interacting Variables for Generalized Linear Models via Neural Networks](https://mlsanity.com/paper/2209.08030/) (2022)

## Built on by (most cited first, 2 of 2)

- [In-Context Learning Enhanced Credibility Transformer](https://mlsanity.com/paper/2509.08122/) (2025)
- [Tab-TRM: Tiny Recursive Model for Insurance Pricing on Tabular Data](https://mlsanity.com/paper/2601.07675/) (2026)

## Abstract (arXiv)

Modeling feature interactions in tabular data remains a key challenge in predictive modeling, for example, as used for insurance pricing. This paper proposes the Tree-like Pairwise Interaction Network (PIN), a novel neural network architecture that explicitly captures pairwise feature interactions through a shared feed-forward neural network architecture that mimics the structure of decision trees. PIN enables intrinsic interpretability by design, allowing for direct inspection of interaction effects. Moreover, it allows for efficient SHapley's Additive exPlanation (SHAP) computations because it only involves pairwise interactions. We highlight connections between PIN and established models such as GA2Ms, gradient boosting machines, and graph neural networks. Empirical results on the popular French motor insurance dataset show that PIN outperforms both traditional and modern neural networks benchmarks in predictive accuracy, while also providing insight into how features interact with each another and how they contribute to the predictions.