Causal Agent based on Large Language Model (2408.06849v1)

Abstract: LLMs have achieved significant success across various domains. However, the inherent complexity of causal problems and causal theory poses challenges in accurately describing them in natural language, making it difficult for LLMs to comprehend and use them effectively. Causal methods are not easily conveyed through natural language, which hinders LLMs' ability to apply them accurately. Additionally, causal datasets are typically tabular, while LLMs excel in handling natural language data, creating a structural mismatch that impedes effective reasoning with tabular data. This lack of causal reasoning capability limits the development of LLMs. To address these challenges, we have equipped the LLM with causal tools within an agent framework, named the Causal Agent, enabling it to tackle causal problems. The causal agent comprises tools, memory, and reasoning modules. In the tools module, the causal agent applies causal methods to align tabular data with natural language. In the reasoning module, the causal agent employs the ReAct framework to perform reasoning through multiple iterations with the tools. In the memory module, the causal agent maintains a dictionary instance where the keys are unique names and the values are causal graphs. To verify the causal ability of the causal agent, we established a benchmark consisting of four levels of causal problems: variable level, edge level, causal graph level, and causal effect level. We generated a test dataset of 1.3K using ChatGPT-3.5 for these four levels of issues and tested the causal agent on the datasets. Our methodology demonstrates remarkable efficacy on the four-level causal problems, with accuracy rates all above 80%. For further insights and implementation details, our code is accessible via the GitHub repository https://github.com/Kairong-Han/Causal_Agent.

• The paper presents a novel causal agent framework integrating causal analysis tools within large language models to convert tabular data into actionable insights.
• It employs a multi-module design featuring tools, memory, and reasoning modules to achieve high accuracy across variable, edge, graph, and causal effect levels.
• Benchmark results demonstrate robust performance with up to 99.4% accuracy in conditional independence tests and over 93.8% in causal effect estimation.

An Expert Overview of the Paper: "Causal Agent Based on LLM"

The paper "Causal Agent Based on LLM" addresses a significant limitation inherent in LLMs: their inability to effectively handle causal inference using tabular datasets. This research proposes an inventive framework aimed at equipping LLMs with the capability to tackle causal problems by integrating them into a structured agent, named the Causal Agent. Its architecture comprises tools, memory, and reasoning modules that work in unison to translate tabular data into actionable causal insights.

Key Contributions

1. Framework for Causal Problem Modeling: The paper establishes a hierarchical modeling approach for LLMs to address causal problems. The problems are categorized into four levels based on complexity and focus: variable level, edge level, causal graph level, and causal effect level.
2. Integration with Causal Tools: To translate tabular data into causal insights, the Causal Agent leverages a suite of causal analysis tools. These tools allow the agent to convert tabular data into natural language summaries, conduct detailed causal analysis, and make accurate causal inferences.
3. Benchmark and Validation: The authors introduced a comprehensive benchmark to validate the causal reasoning capabilities of the Causal Agent. They created a dataset with four levels of causal problems and demonstrated that the agent achieved accuracy rates above 80% across all levels—with notable performances including over 92% accuracy in variable level tests and over 93% accuracy in causal effect estimation.

Technical Implementation

The agent utilizes causal analysis libraries such as causal-learn and EconML, leveraging their functionalities within the agent's reasoning processes. The agent employs the following steps and tools within its modules:

• Tools Module: This module interfaces with causal analysis libraries to conduct independence tests, generate causal graphs, and compute causal effects using the Double Machine Learning (DML) algorithm. The tool encapsulates outputs in a format that LLMs can comprehend and use for further reasoning.
• Reasoning Module: Integrating reflection-based reasoning inspired by the ReAct framework, the agent iterates over causal problems, invoking the necessary tools in sequence until it deduces accurate answers.
• Memory Module: Short-term memory is implemented to store intermediate causal graphs and results. This allows the agent to refer back to previously generated outputs during iterative reasoning processes without requiring re-computation.

Results and Performance

The experimental results underscore the efficacy of the proposed agent across various causal reasoning tasks. The accuracy metrics reflect the agent's robust performance:

• Variable Level: The agent achieved an average accuracy rate of 92.6% in independent tests and 99.4% in conditional independence tests.
• Edge Level: Performance included 89.5% accuracy in identifying direct causal relationships and 97.4% in identifying collider structures.
• Causal Graph Level: For generating causal graphs, the agent recorded 81.8% accuracy for total causal graphs and 91.6% for partial causal graphs.
• Causal Effect Level: The agent demonstrated 93.8% accuracy in calculating the average treatment effect.

Implications and Future Directions

The Causal Agent's framework sets a precedent for integrating advanced causal reasoning into LLMs, significantly enhancing their applicability in domains requiring precise causal inference. This approach offers substantial practical implications:

• Trust in AI Decisions: By augmenting LLMs with causal analysis capabilities, the framework fosters more transparent and interpretable AI decisions.
• Application across Domains: With high accuracy in diverse tasks, this agent can be deployed in fields like healthcare and market analysis to derive meaningful, data-driven causal insights.

Future research could explore:

• Enhanced Model Selection: The agent currently relies on simple models like PC and LinearDML. Extending this to include dynamic model selection based on data properties could further refine its capabilities.
• Domain Adaptation: As performance variations across different domains have been observed, tailoring the causal agent for specific fields could enhance robustness and applicability.
• Long-Term Memory Integration: Including long-term memory could help in retaining knowledge over extended interactions, improving the agent’s cumulative learning and decision-making.

In conclusion, the proposed Causal Agent framework significantly advances the capability of LLMs to handle and reason about causal problems, presenting a promising avenue for future AI applications where understanding causality is paramount.