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OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Framework

Wei Zhou, Hong Huang, Guowen Zhang, Ruize Shi, Kehan Yin, Yuanyuan Lin, Bang Liu

TL;DR

This work tackles interpretability in LLMs by reframing it through causal discovery, introducing two metrics—Causal Structure Distance $CSD$ and Causal Effect Distance $CED$—to quantify differences in structure and causal effects. It also presents the Open Causal Discovery Benchmark (OCDB), a real-data, framework-enabled platform with diverse datasets, baseline models, and evaluation metrics to enable fair comparisons between DAGs and CPDAGs. Empirical results show current algorithms struggle to generalize to real-world data, and that $CSD$/$CED$ offer more reliable interpretability assessments than existing structure- or intervention-focused metrics. The framework and metrics aim to drive development of more trustworthy, interpretable LLMs by standardizing evaluation and promoting broader, real-world benchmarking.

Abstract

Large language models (LLMs) have excelled in various natural language processing tasks, but challenges in interpretability and trustworthiness persist, limiting their use in high-stakes fields. Causal discovery offers a promising approach to improve transparency and reliability. However, current evaluations are often one-sided and lack assessments focused on interpretability performance. Additionally, these evaluations rely on synthetic data and lack comprehensive assessments of real-world datasets. These lead to promising methods potentially being overlooked. To address these issues, we propose a flexible evaluation framework with metrics for evaluating differences in causal structures and causal effects, which are crucial attributes that help improve the interpretability of LLMs. We introduce the Open Causal Discovery Benchmark (OCDB), based on real data, to promote fair comparisons and drive optimization of algorithms. Additionally, our new metrics account for undirected edges, enabling fair comparisons between Directed Acyclic Graphs (DAGs) and Completed Partially Directed Acyclic Graphs (CPDAGs). Experimental results show significant shortcomings in existing algorithms' generalization capabilities on real data, highlighting the potential for performance improvement and the importance of our framework in advancing causal discovery techniques.

OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Framework

TL;DR

This work tackles interpretability in LLMs by reframing it through causal discovery, introducing two metrics—Causal Structure Distance and Causal Effect Distance —to quantify differences in structure and causal effects. It also presents the Open Causal Discovery Benchmark (OCDB), a real-data, framework-enabled platform with diverse datasets, baseline models, and evaluation metrics to enable fair comparisons between DAGs and CPDAGs. Empirical results show current algorithms struggle to generalize to real-world data, and that / offer more reliable interpretability assessments than existing structure- or intervention-focused metrics. The framework and metrics aim to drive development of more trustworthy, interpretable LLMs by standardizing evaluation and promoting broader, real-world benchmarking.

Abstract

Large language models (LLMs) have excelled in various natural language processing tasks, but challenges in interpretability and trustworthiness persist, limiting their use in high-stakes fields. Causal discovery offers a promising approach to improve transparency and reliability. However, current evaluations are often one-sided and lack assessments focused on interpretability performance. Additionally, these evaluations rely on synthetic data and lack comprehensive assessments of real-world datasets. These lead to promising methods potentially being overlooked. To address these issues, we propose a flexible evaluation framework with metrics for evaluating differences in causal structures and causal effects, which are crucial attributes that help improve the interpretability of LLMs. We introduce the Open Causal Discovery Benchmark (OCDB), based on real data, to promote fair comparisons and drive optimization of algorithms. Additionally, our new metrics account for undirected edges, enabling fair comparisons between Directed Acyclic Graphs (DAGs) and Completed Partially Directed Acyclic Graphs (CPDAGs). Experimental results show significant shortcomings in existing algorithms' generalization capabilities on real data, highlighting the potential for performance improvement and the importance of our framework in advancing causal discovery techniques.
Paper Structure (28 sections, 3 theorems, 16 equations, 3 figures, 5 tables, 1 algorithm)

This paper contains 28 sections, 3 theorems, 16 equations, 3 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related work
  3. Causal discovery benchmarks.
  4. Evaluation metrics for causal graphs.
  5. Metrics for interpretability
  6. Causal structure distance
  7. Causal effect distance
  8. Open causal discovery benchmark (OCDB)
  9. Framework design and implementation
  10. Usage of OCDB
  11. Real-world datasets, baseline models and evaluation metrics
  12. Experiments
  13. Experimental results
  14. Conclusion
  15. Limitations
  16. ...and 13 more sections

Key Result

Lemma 1

Let $G$ is a causal graph, for any two distinct nodes $i$ and $j$, if $\mathcal{G}(i, j) = 1$, then the causal effect from $i$ to $j$ is If $\mathcal{G}(i, j) = 0$, then where $do(i=1)$ represents an intervention on variable $i$, while $do(i=0)$ denotes no intervention.

Figures (3)

  • Figure 1: A toy example of all incorrect situations on the predicted graph $A$ for the ground truth causal graph $G$.
  • Figure 2: (a) The framework of interfaces in OCDB. (b) OCDB usage example.
  • Figure 3: The impact of the number of variables $N_V$ on CED computation time.

Theorems & Definitions (10)

  • Definition 1: Causal Structure Distance
  • Example 1
  • Example 2
  • Definition 2: Matrix Form of CSD
  • Definition 3: Causal Effect Distance
  • Definition 4: Reachability Matrix
  • Lemma 1: Causal Effect Estimation
  • Lemma 2: Characterization of valid Adjustment Sets
  • Definition 5: Equivalent Definition of CED
  • Proposition 1: Matrix Form of CED