# Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction

arXiv: https://arxiv.org/abs/2312.16113 · HTML: https://mlsanity.com/paper/2312.16113/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in General knowledge distillation methods, one of 12 directions in Knowledge distillation.
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Placed in

- Knowledge distillation / [General knowledge distillation methods](https://mlsanity.com/distill/general-knowledge-distillation-methods/)

## 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)
- [Adversarial Attacks and Defences: A Survey](https://mlsanity.com/paper/1810.00069/) (2018)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [Neural Network Attributions: A Causal Perspective](https://mlsanity.com/paper/1902.02302/) (2019)
- [A Tutorial on Learning With Bayesian Networks](https://mlsanity.com/paper/2002.00269/) (2020)
- [Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks](https://mlsanity.com/paper/1810.00656/) (2018)
- [k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)](https://mlsanity.com/paper/2004.04523/) (2020)
- [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)
- [Regularization Methods for High-Dimensional Instrumental Variables Regression With an Application to Genetical Genomics](https://mlsanity.com/paper/1304.7829/) (2013)
- [BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks](https://mlsanity.com/paper/2305.03289/) (2023)

## Built on by (most cited first, 9 of 9)

- [A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions](https://mlsanity.com/paper/2406.03712/) (2024)
- [Large Language Models for Causal Discovery: Current Landscape and Future Directions](https://mlsanity.com/paper/2402.11068/) (2024)
- [A Causal Explainable Guardrails for Large Language Models](https://mlsanity.com/paper/2405.04160/) (2024)
- [Professional Agents -- Evolving Large Language Models into Autonomous Experts with Human-Level Competencies](https://mlsanity.com/paper/2402.03628/) (2024)
- [Causal Inference with Latent Variables: Recent Advances and Future Prospectives](https://mlsanity.com/paper/2406.13966/) (2024)
- [Debiasing Machine Unlearning with Counterfactual Examples](https://mlsanity.com/paper/2404.15760/) (2024)
- [Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting](https://mlsanity.com/paper/2407.19697/) (2024)
- [ReFrame: Rectification Framework for Image Explaining Architectures](https://mlsanity.com/paper/2506.18272/) (2025)
- [Causal Interventional Prediction System for Robust and Explainable Effect Forecasting](https://mlsanity.com/paper/2407.19688/) (2024)

## Abstract (arXiv)

Since artificial intelligence has seen tremendous recent successes in many areas, it has sparked great interest in its potential for trustworthy and interpretable risk prediction. However, most models lack causal reasoning and struggle with class imbalance, leading to poor precision and recall. To address this, we propose a Task-Driven Causal Feature Distillation model (TDCFD) to transform original feature values into causal feature attributions for the specific risk prediction task. The causal feature attribution helps describe how much contribution the value of this feature can make to the risk prediction result. After the causal feature distillation, a deep neural network is applied to produce trustworthy prediction results with causal interpretability and high precision/recall. We evaluate the performance of our TDCFD method on several synthetic and real datasets, and the results demonstrate its superiority over the state-of-the-art methods regarding precision, recall, interpretability, and causality.