Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation

Abstract: The reasoning abilities of LLMs are attracting increasing attention. In this work, we focus on causal reasoning and address the task of establishing causal relationships based on correlation information, a highly challenging problem on which several LLMs have shown poor performance. We introduce a prompting strategy for this problem that breaks the original task into fixed subquestions, with each subquestion corresponding to one step of a formal causal discovery algorithm, the PC algorithm. The proposed prompting strategy, PC-SubQ, guides the LLM to follow these algorithmic steps, by sequentially prompting it with one subquestion at a time, augmenting the next subquestion's prompt with the answer to the previous one(s). We evaluate our approach on an existing causal benchmark, Corr2Cause: our experiments indicate a performance improvement across five LLMs when comparing PC-SubQ to baseline prompting strategies. Results are robust to causal query perturbations, when modifying the variable names or paraphrasing the expressions.

• The paper presents a novel NL-CD prompting strategy that decomposes causal inference into explicit, stepwise sub-tasks, improving reasoning accuracy.
• It integrates the PC algorithm by breaking the problem into manageable stages such as graph initialization, skeleton construction, and edge orientation.
• Benchmark evaluations across models like GPT-3.5-turbo and GPT-4-turbo confirm the approach's robustness and resilience to query phrasing variations.

Prompting Strategies for Enabling LLMs to Infer Causation from Correlation

The paper "Prompting Strategies for Enabling LLMs to Infer Causation from Correlation" addresses the challenging task of causal reasoning within the context of LLMs. The authors propose a novel prompting strategy aimed at enhancing these models' ability to distinguish causation from mere correlation, particularly using the framework of the PC algorithm, a well-established causal discovery method.

Overview

LLMs such as GPT-3 and similar AI systems have shown significant advancements across various domains of reasoning, including arithmetic and commonsense reasoning. However, their capacity to understand causal relationships remains limited, especially when tasked with inferring causality solely from correlation statements. This paper advocates a structured prompting strategy that involves breaking down the problem into a series of smaller, more manageable sub-tasks, each associated with a specific step within the causal inference algorithm known as the PC algorithm. This decomposition not only facilitates the reasoning process for the LLM but also ensures that the logical steps are transparent and interpretable.

Methodology

The authors' approach, referred to as the "NL-CD" prompting strategy, draws a parallel to structured causal discovery processes. By decomposing the task, they guide the LLM step-by-step through each phase of causal inference:

1. Initialization: Start with a fully connected undirected graph.
2. Skeleton Construction: Eliminate edges based on provided conditional independence information.
3. Orientation for V-structures: Identify and situate directed arrows to account for colliders.
4. Orientation Completion: Further enhance the graph by ensuring no additional v-structures are mistakenly formed.

The process is augmented with few-shot examples that demonstrate each sub-step, helping the model learn through example rather than requiring extensive retraining.

Results

When evaluated on existing causal reasoning benchmarks, the proposed prompting strategy demonstrated a marked improvement over traditional prompting methods. This includes robust results across different LLMs such as Gemini Pro, Gemini Ultra, PaLM 2, GPT-3.5-turbo, and GPT-4-turbo. Notably, this approach was resistant to variations in query phrasing, reinforcing its utility in practical applications where input data might be variable or less structured.

Implications and Future Directions

This research underscores the importance of structural decomposition in enhancing LLM capabilities in complex reasoning tasks, like causal inference. Such methodologies not only improve accuracy and robustness but also offer transparency in model decision-making processes—an essential feature for applications requiring explainability.

Future work could expand the benchmarking to include more complex datasets with naturally occurring stories or scenarios to evaluate LLMs' causal reasoning further in context-rich environments. Additionally, integrating this method with other AI advancements, such as tool usage within models, may further bridge the gap between algorithmic and human-like understanding of causality.

In conclusion, the authors' structured prompting strategy offers a promising direction for improving causal reasoning in LLMs, paving the way for more reliable and interpretable applications of AI in fields where understanding causation, not just correlation, is critical.