Can AI Uncover Cause and Effect?

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Author: Denis Avetisyan

New research evaluates the ability of artificial intelligence to identify causal relationships within complex datasets, revealing both promise and limitations.

This study benchmarks open-source large language models on pairwise causal discovery tasks in biomedical and multi-domain contexts, highlighting challenges with complex, implicit causal structures.

Despite advances in artificial intelligence, reliably discerning cause-and-effect relationships from natural language remains a significant challenge. This is explored in ‘Benchmarking LLMs for Pairwise Causal Discovery in Biomedical and Multi-Domain Contexts’, which evaluates the capacity of 13 open-source large language models to identify and extract causal links from text. Our results reveal substantial deficiencies, with the best performing models achieving only around 47-50% accuracy on this task, particularly when faced with implicit relationships or multi-sentence contexts. Given the critical need for causal reasoning in fields like healthcare, can we develop more robust prompting strategies or model architectures to unlock the full potential of LLMs for complex causal discovery?

The Challenge of Discerning True Causality

Historically, discerning true cause-and-effect relationships from mere observation has proven remarkably challenging. Conventional statistical techniques, while adept at identifying correlations, frequently stumble when tasked with determining whether one factor actually drives another. This limitation arises because observational datasets often reflect complex webs of interconnected variables, making it difficult to isolate the specific influences at play. Consequently, predictions built on correlational data can be misleading, and interventions designed to address problems based on such data may prove ineffective – or even counterproductive. The inability to reliably infer causality thus significantly restricts the power of data analysis to generate genuinely actionable insights, particularly in domains where understanding ‘why’ is as important as knowing ‘what’.

Real-world systems, and biomedicine in particular, present a unique challenge to identifying true causal links due to their inherent complexity and interconnectedness. Unlike controlled laboratory settings, biological processes involve countless interacting variables – genes, proteins, environmental factors, lifestyle choices – creating a web of associations where correlation does not equal causation. Existing statistical methods often falter when faced with such high dimensionality and confounding factors, leading to inaccurate models and potentially flawed interventions. Consequently, there’s a growing need for discovery approaches that can not only handle this complexity but also scale to accommodate the vast amounts of data now generated by genomic studies, electronic health records, and wearable sensors. These advanced methods aim to move beyond simply identifying associations to uncovering the underlying causal mechanisms driving disease and health, ultimately enabling more effective and targeted therapies.

Distinguishing correlation from causation is paramount, particularly when addressing issues of health and well-being. Simply observing a relationship between two factors doesn’t reveal whether one directly influences the other, or if a third, unmeasured variable is responsible. Mistaking correlation for causation can lead to ineffective – and potentially harmful – interventions; for example, a perceived link between a dietary supplement and improved health might actually be due to individuals who take the supplement also leading generally healthier lifestyles. Therefore, robust methods for causal inference are not merely academic exercises, but essential tools for formulating evidence-based policies, optimizing treatments, and ultimately, improving public health outcomes. Accurate identification of causal factors allows for targeted interventions, maximizing impact and ensuring resources are allocated effectively to address the root causes of disease and promote lasting well-being.

Large Language Models: A New Lens for Causal Reasoning

Large Language Models (LLMs) present a new method for causal discovery by utilizing their inherent capacity to process and interpret natural language. Traditional causal inference techniques often require structured data and explicit variable definitions; LLMs, conversely, can extract potential causal relationships directly from unstructured text sources like articles, reports, and web pages. This is achieved by framing causal questions as language modeling tasks, where the LLM predicts likely causal links based on patterns and associations learned from vast textual datasets. The models’ ability to understand semantic relationships and contextual nuances allows them to go beyond simple correlational analysis and identify plausible causal mechanisms expressed in natural language, offering a pathway to causal inference from data previously inaccessible to automated analysis.

Large Language Models (LLMs) facilitate causal relationship identification from unstructured data by reformulating causal inference as a text prediction problem. Instead of explicitly programming causal algorithms, LLMs are prompted with textual data describing scenarios and asked to predict outcomes or explain relationships. This leverages the model’s pre-trained knowledge of language and world events to infer potential causal links present within the text. For example, an LLM can analyze news articles or scientific abstracts and, given a prompt, identify statements suggesting that one event consistently precedes or influences another, thereby suggesting a causal connection. The model doesn’t discover causality in a traditional statistical sense, but rather identifies statistically likely relationships as expressed through language, allowing for the extraction of potential causal hypotheses from otherwise inaccessible textual data.

Effective causal discovery with Large Language Models necessitates strategies to move beyond correlational pattern recognition. Simple prompting techniques often lead to the identification of statistical associations misinterpreted as causal links. Architectural choices, such as incorporating mechanisms for counterfactual reasoning or explicitly modeling interventions, are crucial for distinguishing causation from correlation. Furthermore, the quality of training data and the specific framing of prompts significantly impact performance; ambiguous or biased prompts can reinforce spurious relationships. Techniques like contrastive learning and the inclusion of domain-specific knowledge can help mitigate these issues and improve the reliability of LLM-based causal inference.

Benchmarking LLM Performance: A Rigorous Evaluation Protocol

A gold standard dataset for evaluating large language model (LLM) performance in causal discovery was established through human annotation. This process utilized a defined annotation protocol to ensure consistency and reliability amongst annotators. Inter-annotator agreement, measured using Cohen’s kappa (κ), reached a value of ≥ 0.758, indicating substantial agreement and high quality of the generated labels. This dataset serves as a benchmark for assessing the accuracy and robustness of LLMs in identifying causal relationships, providing a reliable basis for comparison across different models and prompting strategies.

Evaluation of Large Language Models (LLMs) for causal discovery incorporates analysis of both explicitly stated causal relationships – those directly articulated within the source text – and implicitly expressed connections requiring inference. Explicit causal links are identified through direct linguistic indicators such as “because,” “causes,” or “leads to,” while implicit relationships necessitate the model to deduce causality from contextual information and background knowledge. This dual assessment approach is critical, as real-world causal reasoning frequently involves interpreting nuanced language and uncovering hidden connections beyond directly stated facts, thus providing a more comprehensive understanding of LLM capabilities.

To mitigate the need for extensive labeled datasets, this work leverages zero-shot and few-shot learning paradigms for evaluating Large Language Models (LLMs) in causal discovery. Zero-shot learning assesses LLM performance without any task-specific training examples, while few-shot learning provides a limited number of examples to guide the model. Crucially, these approaches are combined with chain-of-thought prompting, a technique that encourages the LLM to explicitly articulate its reasoning process step-by-step. This prompting strategy aims to improve the model’s ability to generalize to new causal relationships by mimicking a human-like thought process and thereby enhancing performance with limited training data.

Current Large Language Models (LLMs) demonstrate an Average Detection Accuracy of 30-40% when applied to pairwise causal discovery tasks, representing a substantial performance gap compared to human annotators who achieve approximately 95% accuracy on the same tasks. While overall performance remains limited, certain models exhibit improved capabilities; specifically, the Mixtral-8x7B-I-0.1 model achieves an accuracy of approximately 68.06% on tasks that include explicit causal markers, suggesting that clearly defined causal relationships are more readily identified by these models than implicit or inferred connections.

Investigations into DeepSeek models and Mixture-of-Experts (MoE) architectures represent ongoing efforts to enhance large language model (LLM) performance in causal reasoning tasks. DeepSeek models, characterized by their extensive training datasets and parameter scales, aim to improve general knowledge acquisition relevant to identifying causal relationships. MoE architectures, which dynamically activate subsets of parameters based on input, offer a potential solution to the computational challenges of scaling LLMs while maintaining or improving performance on complex reasoning tasks, including causal discovery. Preliminary results suggest that models utilizing these architectures demonstrate improved capabilities compared to standard transformer models, though significant performance gaps remain when compared to human-level accuracy.

Toward a More Causal AI: Error Analysis and Future Directions

A thorough examination of errors made by large language models in discerning causal relationships reveals a consistent challenge with subtlety and contextual understanding. The study pinpointed that over one-third – a substantial 35.7% – of inaccuracies arise from the models failing to identify crucial connections between events. This suggests that while LLMs can often recognize patterns, they struggle when causal links aren’t explicitly stated or require deeper inference based on background knowledge. The findings underscore that successful causal discovery isn’t merely about identifying correlations, but rather about grasping the underlying mechanisms and dependencies – a task that demands more than simply processing textual data, and highlights the need for models to effectively integrate and reason with implicit information.

The efficacy of large language models in discerning causal relationships is profoundly influenced by the quality of training data and the precision of prompting techniques. This research demonstrates that meticulous dataset curation – ensuring comprehensive representation and minimizing inherent biases – is crucial for achieving reliable results. Furthermore, the study emphasizes that carefully crafted prompts, designed to elicit specific reasoning processes, significantly improve a model’s ability to generalize beyond the training data. By addressing potential biases within datasets and employing strategic prompt engineering, researchers can unlock the full potential of LLMs for causal discovery and enhance their applicability to real-world scenarios, ultimately fostering more robust and trustworthy artificial intelligence systems.

The demonstrated capacity of large language models to discern causal relationships suggests a powerful synergy with established knowledge representation and reasoning frameworks. Integrating LLM-based causal discovery with existing knowledge graphs allows for a dynamic updating of factual information and the identification of previously unknown connections, moving beyond static datasets. This combination promises to enhance the robustness of reasoning systems, enabling them to not only process known facts but also infer new relationships and validate existing ones. Such integrated systems could facilitate more accurate predictions, improved decision-making in complex scenarios, and a more nuanced understanding of the underlying mechanisms driving observed phenomena, potentially revolutionizing fields reliant on causal inference.

Ongoing research endeavors are directed towards refining the capabilities of large language models in discerning causal relationships within increasingly intricate systems. Current limitations necessitate exploration beyond static datasets, with future studies designed to incorporate temporal dynamics and feedback loops characteristic of real-world phenomena. This expansion involves developing methodologies for LLMs to not only identify correlations but also to model interventions and predict outcomes in complex, evolving environments. Ultimately, the goal is to move beyond simple causal discovery towards a comprehensive understanding of how systems change over time, potentially integrating LLMs with simulation tools and real-time data streams to facilitate predictive modeling and informed decision-making in fields ranging from epidemiology to climate science.

The study meticulously details the challenges large language models face when discerning complex causal relationships, particularly within nuanced biomedical data. This echoes a sentiment articulated by Ken Thompson: “Software is like entropy: It is difficult to stop it from becoming messy.” The increasing complexity of these models, and the data they process, inevitably introduces a degree of ‘messiness’ – an inability to reliably extrapolate beyond simple correlations to true causal understanding. The research highlights that prompt engineering, while helpful, cannot fully resolve this issue; the fundamental architecture and training data limitations constrain the models’ capacity to grasp implicit causal structures. Just as a well-designed system anticipates cascading effects, the paper demonstrates how failures in causal reasoning can propagate through complex datasets, underscoring the need for more robust and transparent causal discovery methods.

What Lies Ahead?

The pursuit of causal inference via large language models reveals a familiar pattern: apparent competence on curated tasks masks a fragility when confronted with the inherent messiness of biological systems. This work demonstrates that while models can identify direct causal links in simplified scenarios, the capacity to unravel complex, often implicit, relationships within real-world healthcare data remains stubbornly out of reach. Every new dependency – each prompt refinement, each additional parameter – represents the hidden cost of this freedom from true understanding.

Future efforts must move beyond superficial accuracy metrics. The focus should shift towards evaluating a model’s capacity to generalize, to identify not just that a relationship exists, but why it exists, and how that relationship changes within different contexts. A crucial area of exploration is the development of benchmarks that intentionally incorporate ambiguity and require models to reason about underlying mechanisms, not merely pattern-match observed correlations.

Ultimately, the structure of these models dictates their behavioral limits. Current architectures, optimized for prediction, are ill-equipped for true causal reasoning. A fundamental rethinking of model design – one that prioritizes representational clarity and mechanistic transparency – will be essential if this approach is to yield genuine insights into the intricate causal web of life.

Original article: https://arxiv.org/pdf/2601.15479.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-23 21:00