Can Language Models Truly Reason About Cause and Effect?

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Author: Denis Avetisyan

New research reveals a method for rigorously evaluating whether large language models’ causal statements align with underlying causal relationships.

The system rigorously verifies the semantic equivalence of causal expressions generated by a model to ground truth, employing do-calculus and probabilistic reasoning to explore all valid derivations within a given directed acyclic graph-a method that surpasses simple string matching in its capacity for formal validation and ensuring logically sound inferences.
The system rigorously verifies the semantic equivalence of causal expressions generated by a model to ground truth, employing do-calculus and probabilistic reasoning to explore all valid derivations within a given directed acyclic graph-a method that surpasses simple string matching in its capacity for formal validation and ensuring logically sound inferences.

Researchers introduce DoVerifier, a symbolic verification framework leveraging do-calculus and causal graphs to assess the formal correctness of LLM-generated causal expressions.

Current benchmarks for evaluating large language models on causal reasoning often prioritize superficial string matching over formal validity, potentially obscuring genuinely correct inferences. To address this limitation, we present ‘Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification’, introducing DoVerifier, a framework that leverages do-calculus and probability theory to symbolically verify whether LLM-generated causal expressions are formally derivable from a given causal graph. This allows for the recovery of correct answers masked by surface-level differences, offering a more rigorous assessment of semantic correctness. Can this approach unlock a more trustworthy evaluation of LLMs’ capabilities in complex causal scenarios and facilitate the development of more reliable AI systems?

The Illusion of Understanding: LLMs and Causal Deficiency

Despite their remarkable ability to generate human-quality text, large language models frequently falter when confronted with tasks demanding nuanced causal reasoning. These models excel at identifying statistical correlations within data, but often struggle to differentiate between correlation and causation, leading to logically unsound conclusions. While capable of producing grammatically correct and contextually relevant responses, LLMs can easily generate explanations that appear reasonable but lack a genuine understanding of underlying causal mechanisms. This limitation stems from their training primarily on vast amounts of text, which emphasizes pattern recognition rather than the development of a robust framework for evaluating cause-and-effect relationships; therefore, seemingly coherent outputs may be based on spurious associations rather than true causal principles, highlighting a critical gap in their cognitive abilities.

Current evaluations of causal reasoning in large language models frequently prioritize superficial similarities over genuine understanding. Metrics like string matching and BERTScore, while useful for assessing textual overlap, often fail to identify causally correct responses that are phrased differently from a pre-defined “ground truth.” This limitation stems from a reliance on lexical or semantic similarity, which doesn’t probe whether the model has actually grasped the underlying causal mechanism. A response might accurately describe an outcome, but if it doesn’t demonstrate an understanding of why that outcome occurred – the specific causal factors at play – it reveals a lack of true causal reasoning. Consequently, high scores on these standard metrics can be misleading, masking a fundamental inability to distinguish correlation from causation and hindering progress in building truly intelligent systems.

The limitations of large language models extend beyond simple text generation to a deeper struggle with discerning cause and effect. Current models frequently identify correlations – noting that two events occur together – but lack the capacity to establish genuine causal links. This deficiency stems from an inability to formally represent causal relationships; a statement like ‘A causes B’ requires more than just statistical association. Truly understanding causality demands a system capable of verifying the mechanism by which A influences B, a process that necessitates moving beyond pattern recognition to a formal representation of the underlying process. Without this formalization, models remain vulnerable to spurious correlations and illogical conclusions, hindering their ability to reason effectively and provide reliable explanations for observed phenomena.

Current evaluation metrics like BLEU, token-level F1, BERTScore, and string matching struggle with causal reasoning because logically equivalent expressions can be penalized for surface-level differences while inequivalent ones can receive high scores due to shared tokens.
Current evaluation metrics like BLEU, token-level F1, BERTScore, and string matching struggle with causal reasoning because logically equivalent expressions can be penalized for surface-level differences while inequivalent ones can receive high scores due to shared tokens.

DoVerifier: A Framework for Symbolic Causal Validation

DoVerifier is a symbolic verification framework intended to determine the equivalence of causal expressions. It achieves this by leveraging the rules of do-calculus – a set of rules for reasoning about interventions in causal models – alongside established probability rules. The framework operates on the principle that a causal expression is considered valid if it can be systematically derived from a base expression through the application of these defined rules. This process allows for the formal validation of causal claims, providing a rigorous method for assessing the correctness of causal reasoning without relying on empirical data or assumptions about the underlying data distribution.

DoVerifier employs a derivation graph to facilitate the verification of causal expressions. This graph is constructed by representing each valid application of a do-calculus or probability rule as a node, with edges indicating the derivation step. The initial causal expression serves as the root node, and subsequent nodes represent expressions derived through rule applications. This structure allows for a step-by-step, traceable verification process, where each path from the root to a target expression represents a potential proof sequence. The graph comprehensively captures all possible derivations, enabling the framework to systematically explore the solution space and determine the validity of a given causal claim.

DoVerifier employs Breadth-First Search (BFS) to traverse the derivation graph constructed for a given causal expression. BFS systematically explores all possible derivation paths level by level, starting from the initial expression. This approach guarantees that the shortest proof sequence – if one exists – will be identified. Each node in the graph represents a state of the derivation, and edges represent the application of do-calculus or probability rules. If BFS successfully reaches a target state representing the desired causal expression, a valid proof sequence is confirmed; otherwise, the search terminates indicating the expression is not valid under the applied rules. The efficiency of BFS is crucial for handling complex causal expressions and maintaining reasonable verification times.

Formalizing Causality: Do-Calculus, Causal Graphs, and D-Separation

DoVerifier’s central functionality is built upon do-calculus, a formal system developed by Judea Pearl for reasoning about interventions and causal effects. This calculus provides a set of rules – including do-calculus rules 1-3 – that allow for the manipulation of causal expressions, specifically those involving the [latex]do()[/latex] operator which represents an intervention setting a variable to a specific value. These rules enable the determination of whether two different causal expressions are logically equivalent, meaning they yield the same results under any possible causal model. The application of do-calculus within DoVerifier facilitates the validation of causal claims by rigorously assessing the validity of the underlying causal reasoning, independent of observational data alone. This manipulation is crucial for tasks like counterfactual reasoning and identifying potential confounders in causal analysis.

DoVerifier employs a CausalGraph, a directed acyclic graph (DAG), to visually and mathematically define the relationships between variables under investigation. Nodes in the graph represent variables, and directed edges represent direct causal influences; the absence of an edge signifies conditional independence given other variables. This graphical representation allows for the formalization of causal assumptions and provides a basis for applying do-calculus rules. The CausalGraph serves as a prerequisite for evaluating the validity of causal claims by explicitly outlining the assumed causal structure, enabling the identification of potential confounding factors and mediating pathways that must be accounted for in causal inference.

D-separation, a core component of do-calculus, formally defines when two sets of variables are conditionally independent given a third set. This determination relies on identifying all blocked paths between the variables of interest in a causal graph; a blocked path indicates conditional independence. Specifically, d-separation assesses if all paths between two variables are blocked by a set of observed variables, meaning that knowing the values of the observed variables renders the two variables independent. [latex]X \perp Y | Z[/latex] signifies that X is independent of Y given Z, and this independence is verified through d-separation analysis on the underlying causal graph. Accurate d-separation calculations are crucial for ensuring the validity of causal inferences derived from do-calculus, as they establish the necessary conditions for manipulating causal expressions and drawing reliable conclusions.

Semantic Equivalence: Validating Causal Claims Beyond Surface-Level Matching

DoVerifier centers on the concept of [latex]CausalExpressions[/latex], which formally articulate the distinction between how a system behaves under natural conditions – the [latex]ObservationalDistribution[/latex] – and how it responds to deliberate manipulation – the [latex]InterventionalDistribution[/latex]. These expressions aren’t simply about noting correlations; they define the effect of an intervention, representing a shift in probability distributions caused by actively setting a variable’s value. By explicitly modeling this difference, DoVerifier moves beyond passively observing data to actively reasoning about cause and effect, allowing it to assess whether a proposed causal relationship accurately reflects the underlying mechanisms at play and ultimately validating causal claims with greater precision.

The DoVerifier framework establishes whether two distinct causal expressions articulate the same underlying relationship through a process of semantic equivalence verification. Rather than simply comparing the surface-level wording, the framework assesses if one expression can be logically derived from the other, given the constraints and connections depicted in a provided causal graph. This derivation isn’t about syntactic similarity; expressions may differ in their phrasing or the specific variables used, yet still represent the same causal effect if they are interconvertible within the graphical model. Essentially, DoVerifier confirms that different ways of stating a causal relationship are, in fact, representations of the identical phenomenon, bolstering the robustness of causal claim validation beyond superficial textual matches.

DoVerifier distinguishes itself through an ability to identify causally valid responses even when those responses differ syntactically from a predetermined “correct” answer. Traditional evaluation metrics, such as string matching and BERTScore, often penalize such variations, leading to an underestimation of a system’s true causal reasoning capability. However, DoVerifier operates by verifying the underlying causal relationship represented in an expression, rather than simply comparing text. This approach allows the framework to recover a substantially larger proportion of correct answers that are expressed differently, resulting in significantly improved recall – a key indicator of the system’s ability to find all relevant and correct solutions – compared to conventional methods. This focus on semantic equivalence, rather than syntactic similarity, marks a substantial advancement in assessing the robustness and accuracy of causal inference systems.

The pursuit of demonstrable correctness, as embodied in this work with DoVerifier, echoes a sentiment long held by mathematical minds. David Hilbert famously stated, “One must be able to say everything that can be said.” This principle, though stated broadly, resonates deeply with the paper’s focus on formal verification of causal reasoning in LLMs. DoVerifier moves beyond merely assessing if an LLM appears to reason correctly, instead verifying if its conclusions are derivably true given a causal graph and the rules of do-calculus. Such a commitment to provability, rather than empirical testing alone, represents a crucial step towards building truly reliable AI systems, ensuring solutions are not merely functional, but fundamentally sound.

Beyond Surface Concordance

The pursuit of intelligence, it seems, has largely settled for superficial resemblance. This work, by anchoring large language model reasoning to the formal logic of do-calculus, attempts a necessary recalibration. However, DoVerifier, while a substantial step towards verifiable causal inference, merely illuminates the chasm between syntactical correctness and genuine understanding. The framework currently relies on a pre-defined causal graph; the automation of graph discovery from unstructured data remains a significant, and frankly, more interesting, challenge. A model can flawlessly manipulate symbols derived from a correct graph and still exhibit a fundamental lack of causal intuition.

Future investigations must address the limitations inherent in translating natural language into formal expressions. The ambiguity of human communication, while often a source of richness, presents a persistent obstacle. Moreover, the scalability of symbolic verification is not guaranteed; complex causal structures will inevitably strain computational resources. A truly robust system will require not only a means of verifying derivations but also of proving the minimality and optimality of those derivations – a demand for elegance that few current approaches even acknowledge.

Ultimately, the field must confront a humbling truth: passing a test, even a formally verified one, does not equate to intelligence. The goal should not be to build systems that simulate reasoning, but to construct algorithms whose correctness can be mathematically proven. Only then can one speak of genuine progress, and perhaps, a glimpse of true artificial intelligence.

Original article: https://arxiv.org/pdf/2601.21210.pdf

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

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2026-02-01 23:59