causal-strength: Measure the Strength Between Cause and Effect

Table of Contents

• 📜 Citation *
• 🌟 Features *
• 🚀 Installation *
  • Prerequisites
  • Steps
• 🛠️ Usage *
  • Quick Start
  • Evaluating Causal Strength
  • Generating Causal Heatmaps
• 📚 References *

📜 Citation

🌟 Features

• Causal Strength Evaluation: Compute the causal strength between two statements using models like CESAR.
• Visualization Tools: Generate heatmaps to visualize attention and similarity scores between tokens.
• Extensibility: Easily add new metrics and models for evaluation.
• Hugging Face Integration: Load models directly from the Hugging Face Model Hub.

🚀 Installation

Prerequisites

• Python 3.7 or higher
• PyTorch (for GPU support, ensure CUDA is properly configured)

Steps

1. Install it directly from PyPI
   pip install causal-strength
2. Install it from source code
   bash

   1git clone https://github.com/cui-shaobo/causal-strength.git 2cd causal-strength 3pip install .

🛠️ Usage

Quick Start

Evaluating Causal Strength

1. For the CESAR model.
   python

   1from causalstrength import evaluate 2 3# Test CESAR Model 4s1_cesar = "Tom is very hungry now." 5s2_cesar = "He goes to McDonald for some food." 6 7print("Testing CESAR model:") 8cesar_score = evaluate(s1_cesar, s2_cesar, model_name='CESAR', model_path='shaobocui/cesar-bert-large') 9print(f"CESAR Causal strength between \"{s1_cesar}\" and \"{s2_cesar}\": {cesar_score:.4f}")
   This will now output the following without errors:
   plaintext

   1Testing CESAR model: 2CESAR Causal strength between "Tom is very hungry now." and "He goes to McDonald for some food.": 0.4482
2. For the CEQ model
   python

   1 from causalstrength import evaluate 2 3 # Test CEQ Model 4 s1_ceq = "Tom is very hungry now." 5 s2_ceq = "He goes to McDonald for some food." 6 7 print("\nTesting CEQ model:") 8 ceq_score = evaluate(s1_ceq, s2_ceq, model_name='CEQ') 9 print(f"CEQ Causal strength between \"{s1_ceq}\" and \"{s2_ceq}\": {ceq_score:.4f}")
   This will now output the following without errors:
   plaintext

   1Testing CEQ model: 2CEQ Causal strength between "Tom is very hungry now." and "He goes to McDonald for some food.": 0.0168

• s1 (str): The cause statement.
• s2 (str): The effect statement.
• model_name (str): The name of the model to use ('CESAR', 'CEQ', etc.).
• model_path (str): Hugging Face model identifier or local path to the model.

Generating Causal Heatmaps

The causal heatmap is as follows:

📚 References

1. Cui, Shaobo, et al. "Exploring Defeasibility in Causal Reasoning." Findings of the Association for Computational Linguistics ACL 2024. 2024.
2. Du, Li, et al. "e-CARE: a New Dataset for Exploring Explainable Causal Reasoning." Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.