Hypothesizing Missing Causal Variables with LLMs (2409.02604v1)

Abstract: Scientific discovery is a catalyst for human intellectual advances, driven by the cycle of hypothesis generation, experimental design, data evaluation, and iterative assumption refinement. This process, while crucial, is expensive and heavily dependent on the domain knowledge of scientists to generate hypotheses and navigate the scientific cycle. Central to this is causality, the ability to establish the relationship between the cause and the effect. Motivated by the scientific discovery process, in this work, we formulate a novel task where the input is a partial causal graph with missing variables, and the output is a hypothesis about the missing variables to complete the partial graph. We design a benchmark with varying difficulty levels and knowledge assumptions about the causal graph. With the growing interest in using LLMs to assist in scientific discovery, we benchmark open-source and closed models on our testbed. We show the strong ability of LLMs to hypothesize the mediation variables between a cause and its effect. In contrast, they underperform in hypothesizing the cause and effect variables themselves. We also observe surprising results where some of the open-source models outperform the closed GPT-4 model.

• The paper introduces a framework where LLMs hypothesize missing variables in causal DAGs, effectively bridging gaps in scientific discovery.
• It outlines four benchmark tasks—from controlled setups to iterative open-world scenarios—to evaluate LLMs' causal reasoning and hypothesis generation.
• Results demonstrate strong performance in mediator identification with models like GPT-4, while also revealing challenges in detecting source and sink variables.

Hypothesizing Missing Causal Variables with LLMs: An Expert's Overview

The paper "Hypothesizing Missing Causal Variables with LLMs" by Ivaxi Sheth, Sahar Abdelnabi, and Mario Fritz introduces a novel problem formulation where LLMs are leveraged to propose hypotheses for missing variables in partially known causal Directed Acyclic Graphs (DAGs). This task aligns with the imperative process of scientific discovery that involves hypothesis generation, experimental design, data evaluation, and continuous refinement. The primary innovation here is harnessing LLMs to bridge gaps in causal understanding, a role traditionally reliant on domain expertise.

Context and Motivation

Scientific advances rely heavily on the elucidation of causal relationships beyond mere correlations. The randomized controlled trials (RCTs) and related methodologies usually necessitate substantial domain knowledge and resource-intensive data collection. By leveraging the extensive training of LLMs, the authors aim to simulate expert knowledge to assist in the early stages of causal discovery, specifically in hypothesizing missing variables in a given causal structure.

Methodological Approach

Tasks and Benchmark Design

The authors systematically introduce four tasks with increasing complexity to evaluate LLMs' ability to hypothesize missing causal variables:

1. Out-of-Context Controlled Variable Identification: This baseline task involves providing LLMs with a partial DAG and a list of irrelevant options (distractors) including the ground truth. The model's ability to correctly hypothesize the missing variable is assessed.
2. In-Context Controlled Variable Identification: Here, the complexity is heightened by including both in-context (related to the graph) and out-of-context distractors. This requires the LLM to discern the most causally plausible variable amidst more confusing options.
3. Hypothesizing in Open World: This task removes multiple-choice constraints, asking the LLM to propose hypotheses for the missing variable without explicit options, thus mimicking realistic scientific scenarios.
4. Iteratively Hypothesizing in Open World: Extending the third task, this setup involves hypothesizing multiple mediators in a causal pathway iteratively, reflecting progressive hypothesis refinement akin to scientific exploration.

Evaluation Metrics

Two primary metrics are utilized to evaluate model outputs:

• Semantic Similarity: Measures the cosine similarity between the embeddings of the model’s suggestions and the ground truth variables.
• LLM-as-Judge: A qualitative measure where the LLM itself evaluates the contextual fit of its own predictions on a scale from 1 to 10.

Results and Implications

Numerical Results

• Task 1 and 2: Models like GPT-4 and Mixtral exhibit strong performance with high accuracy, indicating proficiency in basic causal reasoning. Interestingly, incorporating distractors that are contextually relevant reveals the models' nuanced understanding but also their vulnerabilities.
• Task 3: In open-world scenarios, GPT-4 and Mistral achieve superior semantic similarity scores. However, the performance variance highlights the challenge of domain adaptation, underscoring the models' differential success across diverse datasets.
• Task 4: Iterative hypotheses generation demonstrates that LLMs excel in identifying mediator variables, critical in understanding indirect causal pathways. The introduction of Mediation Influence Scores (MIS) showcases a method to prioritize mediator hypotheses effectively.

Qualitative Analysis

The empirical results are supported by qualitative evaluations through LLM-as-Judge scores, which consistently align with semantic similarity measures. The detailed analysis of node-type specific performances reveals that models are adept at hypothesizing mediators and colliders, whereas identifying sources and sinks remains challenging. This dichotomy underscores the importance of incorporating more domain-specific reinforcement in training future LLMs.

Future Directions

The paper opens multiple avenues for future research:

1. Enhanced Retrieval-Augmented Models: Augmenting LLMs with domain-specific retrieval mechanisms could mitigate the identified gaps in hypothesizing source and sink variables.
2. Iterative Refinement Processes: Developing methodologies to systematically refine LLM-generated hypotheses through iterative feedback could enhance their reliability in practical applications.
3. Cross-Domain Generalization: Exploring LLMs' adaptability to varied scientific domains remains crucial, potentially leveraging transfer learning techniques for improved cross-domain causal inference.

Conclusion

The paper establishes a significant stride in leveraging LLMs for scientific hypothesis generation in incomplete causal structures. By formalizing the task and presenting a detailed benchmark, the authors provide a robust framework for future explorations. Their findings highlight LLMs' potential to act as proxies for domain experts, particularly in identifying complex causal relationships, thereby accelerating the initial phases of scientific discovery. The practical and theoretical implications of this work are profound, suggesting an evolving role for AI in augmenting human expertise in scientific research.