Can Large Language Models Accurately Infer Causation from Correlation?

Core Concepts

Large language models struggle to accurately infer causal relationships from statistical correlations, even when provided with complete information about the correlations.

Abstract

This paper introduces a novel task called CORR2CAUSE to test the pure causal inference skills of large language models (LLMs). The task involves determining the validity of hypothesized causal relationships given a set of statistical correlations among variables. The authors first construct a large-scale dataset of over 200,000 samples, grounded in the formal framework of causal discovery. Each sample consists of a set of correlational statements and a hypothesized causal relationship, with a label indicating whether the inference is valid or not. The authors then evaluate the performance of 17 existing LLMs on this CORR2CAUSE task. The results show that none of the LLMs perform well, with most models achieving close to random-level performance. This suggests a key shortcoming in the causal inference skills of current LLMs. The authors further explore whether LLMs can learn this skill through finetuning on the dataset. While finetuned models demonstrate strong performance on the original test set, they fail to generalize to out-of-distribution settings, such as when the variable names or textual expressions are perturbed. This indicates that the causal inference skills acquired by LLMs through finetuning are not robust. The authors conclude that the CORR2CAUSE task is a challenging benchmark for LLMs, and can help guide future research on improving their pure reasoning skills and generalizability.

Stats

"Causal inference is one of the hallmarks of human intelligence." "The vast majority of studies frame causal reasoning as a skill to navigate around empirical knowledge (Gordon et al., 2012; Sap et al., 2019a;b; Qin et al., 2019; Bhagavatula et al., 2020), and also treat LLMs as a knowledge base when evaluating its causal skills (Kıcıman et al., 2023; Tu et al., 2023; Xie et al., 2023)." "None of the 17 existing LLMs we investigate perform well on this pure causal inference task." "LLMs fail to robustly acquire this skill in out-of-distribution settings."

Quotes

"Causal inference is one of the hallmarks of human intelligence." "LLMs are 'causal parrots,' which recite the causal knowledge in the training data." "CORR2CAUSE is a challenging task for LLMs, and can be helpful in guiding future research on improving LLMs' pure reasoning skills and generalizability."

Key Insights Distilled From

Can Large Language Models Infer Causation from Correlation?

by Zhij... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2306.05836.pdf

Deeper Inquiries

How can we design more comprehensive benchmarks to test the causal reasoning capabilities of LLMs beyond the CORR2CAUSE task?

To design more comprehensive benchmarks for testing the causal reasoning capabilities of Large Language Models (LLMs), we can consider the following strategies: Include a Variety of Causal Reasoning Tasks: Expand the benchmark to include a diverse set of tasks that require different types of causal reasoning, such as counterfactual reasoning, intervention analysis, causal effect estimation, and causal structure learning. This will provide a more holistic evaluation of the LLMs' causal reasoning abilities. Incorporate Real-World Scenarios: Develop scenarios and datasets that reflect real-world causal relationships and complexities. This could involve using data from various domains such as healthcare, economics, social sciences, and environmental studies to test the models' ability to reason about causal effects in practical settings. Consider Multi-hop Reasoning: Design tasks that require LLMs to perform multi-hop reasoning to infer causal relationships across multiple variables or events. This can help evaluate the models' ability to make complex causal inferences that involve indirect relationships. Adversarial Testing: Introduce adversarial examples or perturbations to test the robustness of LLMs in causal reasoning tasks. This can help assess the models' generalization capabilities and resilience to variations in the input data. Fine-Grained Evaluation: Break down the evaluation metrics to assess performance on different types of causal relationships, such as direct causation, confounding factors, mediators, and colliders. This can provide insights into the models' strengths and weaknesses in different aspects of causal reasoning. By incorporating these elements into the benchmark design, we can create a more comprehensive evaluation framework that challenges LLMs to demonstrate a wide range of causal reasoning capabilities.

How can the insights from this work on causal inference be applied to improve the reasoning and decision-making capabilities of LLMs in real-world applications?

The insights from this work on causal inference can be leveraged to enhance the reasoning and decision-making capabilities of Large Language Models (LLMs) in real-world applications in the following ways: Enhanced Understanding of Causality: By improving LLMs' ability to infer causation from correlation, they can better understand the underlying causal relationships in the data they process. This can lead to more accurate and reliable decision-making based on causal reasoning. Robustness and Generalization: Addressing the limitations identified in the CORR2CAUSE task can help LLMs improve their robustness and generalization capabilities in causal inference. This can ensure that the models perform well in diverse and unseen scenarios. Domain-Specific Applications: Applying the insights to domain-specific applications, such as healthcare, finance, or climate science, can enable LLMs to make informed decisions based on causal relationships specific to those domains. This can lead to more effective solutions and recommendations. Counterfactual Reasoning: Incorporating counterfactual reasoning capabilities based on causal inference can allow LLMs to simulate "what-if" scenarios and evaluate the potential outcomes of different actions. This can support better decision-making in complex situations. Ethical and Fair Decision-Making: By understanding causality more deeply, LLMs can make decisions that are fair, unbiased, and ethically sound. This can help mitigate potential biases and ensure that the models' decisions are aligned with ethical principles. Overall, applying the insights from this work can empower LLMs to make more informed, reliable, and ethical decisions across a wide range of real-world applications, ultimately enhancing their utility and impact in various domains.