# Peeling Context from Cause for Molecular Property Prediction

arXiv: https://arxiv.org/abs/2511.06692 · HTML: https://mlsanity.com/paper/2511.06692/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 16 of 16)

- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [SchNet: A continuous-filter convolutional neural network for modeling quantum interactions](https://mlsanity.com/paper/1706.08566/) (2017)
- [Causal inference using invariant prediction: identification and confidence intervals](https://mlsanity.com/paper/1501.01332/) (2015)
- [Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development](https://mlsanity.com/paper/2102.09548/) (2021)
- [Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs](https://mlsanity.com/paper/2202.05441/) (2022)
- [Drug discovery with explainable artificial intelligence](https://mlsanity.com/paper/2007.00523/) (2020)
- [Out-Of-Distribution Generalization on Graphs: A Survey](https://mlsanity.com/paper/2202.07987/) (2022)
- [Molecule Attention Transformer](https://mlsanity.com/paper/2002.08264/) (2020)
- [GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text](https://mlsanity.com/paper/2308.06911/) (2023)
- [Graph Out-of-Distribution Generalization via Causal Intervention](https://mlsanity.com/paper/2402.11494/) (2024)
- [Empowering Graph Invariance Learning with Deep Spurious Infomax](https://mlsanity.com/paper/2407.11083/) (2024)
- [Molecular Property Prediction Based on Graph Structure Learning](https://mlsanity.com/paper/2312.16855/) (2023)
- [Dissecting the Failure of Invariant Learning on Graphs](https://mlsanity.com/paper/2411.02847/) (2024)
- [A Recipe for Causal Graph Regression: Confounding Effects Revisited](https://mlsanity.com/paper/2507.00440/) (2025)

## Abstract (arXiv)

Deep models are used for molecular property prediction, yet they are often difficult to interpret and may rely on spurious context rather than causal structure, which reduces reliability under distribution shift and harms predictive performance. We introduce CLaP (Causal Layerwise Peeling), a framework that separates causal signal from context in a layerwise manner and integrates diverse graph representations of molecules. At each layer, a causal block performs a soft split into causal and non-causal branches, fuses causal evidence across modalities, and progressively removes batch-coupled context to focus on label-relevant structure, thereby limiting shortcut signals and stabilizing layerwise refinement. Across four molecular benchmarks, CLaP consistently improves MAE, MSE, and R2 over competitive baselines. The model also produces atom-level causal saliency maps that highlight substructures responsible for predictions, providing actionable guidance for targeted molecular edits. Case studies confirm the accuracy of these maps and their alignment with chemical intuition. By peeling context from cause at every layer, the model yields predictors that are both accurate and interpretable for molecular design.