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Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction

Zhixuan Chu, Mengxuan Hu, Qing Cui, Longfei Li, Sheng Li

TL;DR

The paper tackles the challenge of trustworthy risk prediction by addressing the lack of causal reasoning and the prevalence of class imbalance in traditional models. It introduces Task-Driven Causal Feature Distillation (TDCFD), which uses the Potential Outcome Framework to distill task-specific causal attributions for each feature via relational graph construction, adaptive group Lasso propensity score estimation, and causal feature attribution estimation, followed by risk prediction using a neural network on these distilled features. The authors provide theoretical guarantees for estimator consistency and unbiased causal estimation, and empirically demonstrate that TDCFD achieves superior precision and recall while offering causal interpretability on both synthetic and real-world datasets. This approach advances trustworthy AI by producing predictions that are not only accurate but also causally explainable, enabling better decision-making in finance, healthcare, and beyond.

Abstract

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.

Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction

TL;DR

The paper tackles the challenge of trustworthy risk prediction by addressing the lack of causal reasoning and the prevalence of class imbalance in traditional models. It introduces Task-Driven Causal Feature Distillation (TDCFD), which uses the Potential Outcome Framework to distill task-specific causal attributions for each feature via relational graph construction, adaptive group Lasso propensity score estimation, and causal feature attribution estimation, followed by risk prediction using a neural network on these distilled features. The authors provide theoretical guarantees for estimator consistency and unbiased causal estimation, and empirically demonstrate that TDCFD achieves superior precision and recall while offering causal interpretability on both synthetic and real-world datasets. This approach advances trustworthy AI by producing predictions that are not only accurate but also causally explainable, enabling better decision-making in finance, healthcare, and beyond.

Abstract

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.
Paper Structure (14 sections, 2 theorems, 3 equations, 4 figures, 3 tables)

This paper contains 14 sections, 2 theorems, 3 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Preliminary
  3. Methodology
  4. Causal Feature Distillation
  5. Relational Graph Construction
  6. PS Estimation by Adaptive Group Lasso
  7. Causal Feature Attribution Estimation
  8. Risk Prediction
  9. Theoretical Analysis
  10. Experiments
  11. Causal Effect Estimation Experiments
  12. Experiments of Risk Prediction on Synthetic
  13. Experiments of Risk Prediction on Real Data
  14. Conclusion

Key Result

Theorem 1

Let $\gamma>0$, $\epsilon>0$, $\lambda_n = \mathcal{O}(n^{-1/4})$, and $\theta_n =\Omega (n^{-\gamma/(4\nu -4)+ \epsilon})$, for any $\delta >0$. Then there exists $N_\delta$ such that for $n > N_\delta$, $d(\hat{\alpha}_n, \mathcal{H^*_{\alpha}}) \le C \left( \frac{\log n}{n}\right)^{\frac{1}{4(\n

Figures (4)

  • Figure 1: Examples of the recall and precision decrease.
  • Figure 2: The directed acyclic graph of risk data generation.
  • Figure 3: The DAGs of risk data generation with and without hidden variables.
  • Figure 4: Original values and causal feature attributions.

Theorems & Definitions (6)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Theorem 1
  • Theorem 2