Causal Understanding by LLM s: The Role of Uncertainty (2025)

Abstract: Recent papers show LLMs achieve near-random accuracy in causal relation classification, raising questions about whether such failures arise from limited pretraining exposure or deeper representational gaps. We investigate this under uncertainty-based evaluation, testing whether pretraining exposure to causal examples improves causal understanding using >18K PubMed sentences—half from The Pile corpus, half post-2024—across seven models (Pythia-1.4B/7B/12B, GPT-J-6B, Dolly-7B/12B, Qwen-7B). We analyze model behavior through: (i) causal classification, where the model identifies causal relationships in text, and (ii) verbatim memorization probing, where we assess whether the model prefers previously seen causal statements over their paraphrases. Models perform four-way classification (direct/conditional/correlational/no-relationship) and select between originals and their generated paraphrases. Results show almost identical accuracy on seen/unseen sentences (p>0.05), no memorization bias (24.8% original selection), output distribution over the possible options almost flat — with entropic values near the maximum (1.35/1.39), confirming random guessing. Instruction-tuned models show severe miscalibration (Qwen: >95% confidence, 32.8% accuracy, ECE=0.49). Conditional relations induce highest entropy (+11% vs direct). These findings suggest that failures in causal understanding arise from the lack of structured causal representation, rather than insufficient exposure to causal examples during pretraining.

## introduction causal understanding from text, intended here as the ability of an llm to identify whether a text includes a statement about a causal relation between two entities, and which is the causal direction of such a relation, is a critical task for modern natural * equal contribution language understanding. previous work demonstrates that large language models (llms) struggle with such causal tasks, achieving near-random performance on benchmarks requiring causal inference @xcite @xcite @xcite . recent works showed the importance of analyzing the underlying model uncertainty to achieve better results, or at least to understand the reasons for poor performances @xcite . from this perspective, a very promising direction is provided by distinguishing between different sources of uncertainty, such as epistemic, corresponding to the uncertainty related to lack of knowledge about the underlying model, and aleatoric, that is the intrinsic ambiguity of the process @xcite . another crucial aspect is how the presence of seen versus unseen data -i.e., content observed during pretraining or familiar observations -affects uncertainty and model behavior in terms of causal understanding. while uncertainty quantification in llms has been explored in prior work @xcite @xcite , the link between uncertainty sources and familiar causal patterns in the context of causal understanding remains underexamined. we design controlled experiments to examine how these uncertainty sources arise in the context of causal understanding. we consider memorization as one of the tasks to understand the effect of seen verbatim causal patterns, in line with the uncertainty sources. using scientific conclusion sentences from pubmed abstracts, we test whether models trained on these exact texts (via the pile dataset, @xcite ) show reduced uncertainty compared to similar but unseen texts and, if this has an impact on their accuracy. our approach uses two complementary tests for memorization effects. first, if models truly understood causal patterns from training (not just sur-face forms), they should exhibit substantially lower uncertainty and higher accuracy on familiar data (i.e., training data) versus unseen data. second, if only surface memorization occurred, we would expect substantial differences in uncertainty between original sentences and their paraphrases within the training dataset. in a nutshell, we use uncertainty metrics to explore memorization effects in causal understanding and whether this reflects representational limitations rather than data exposure. ## related work recent work examines uncertainty sources in llms. @xcite demonstrates that models can assess their uncertainty through verbalized confidence. @xcite proposes semantic entropy to measure uncertainty in freeform generation. however, these approaches focus on general tasks rather than structured reasoning. @xcite shows llms struggle with calibrated uncertainty in knowledge-intensive tasks, consistent with our findings in causal reasoning. memorization's role in llm capabilities re-mains debated. @xcite demonstrate that models memorize training data verbatim, while @xcite show this memorization can be beneficial. @xcite quantifies memorization across model scales. other findings show that memorization alone cannot explain model capabilities, requiring 100+ exact repetitions for reliable recall @xcite . while prior work evaluates causal understanding in llms through various benchmarks, none have examined it through the lens of uncertainty sources and verbatim memorization recalls. our work extends this by showing memorization fails to improve structured reasoning tasks. our approach uniquely combines controlled exposure to training data with uncertainty quantification, revealing that causal understanding requires more than pattern memorization, especially when the complexity increases. the rest of the paper is organized as follows. in section 3, we present the datasets used for the experimental study and analyses. section 4 presents the detailed discussion of the proposed uncertaintybased quantification, and the experimental setup is presented in section 5. results and analysis are reported in section 6, with detailed discussion in section 7. ## data construction first, we used two datasets of sentences labeled with their causal types to test the impact of exposure to causal patterns during pretraining on the accuracy and uncertainty of causal understanding tasks with llms. we used @xcite 's dataset, consisting of 3,061 sentences from science findings classified into four causal relationship types: direct causal, conditional causal, correlational, or no relationship. the original dataset lacked source abstracts for the extracted sentences. as these were needed to train a classifier for extending the dataset with more recent abstracts, we searched pubmed using near-exact sentence matches. this yielded a filtered dataset with the following distribution: direct causal (234), conditional causal (113), correlational (489), and no relationship (598). this resulting dataset is hereafter referred to as original. although filtering removed almost half of the entries, the label distribution remains highly similar to the original (total variation @xmath0.026). future work will consider more sophisticated matching schemes to preserve more data. to control for verbatim memorization, we created an extension from pubmed abstracts published after 2024, beyond our models' training cutoff. we use a bert-based classifier (f1-@xmath1.97) trained on the original annotations to label 5,400 new sentences, then subsample to match the class distribution of our original dataset. this resulting dataset is hereafter referred to as newer we complement the datasets by generating, with gpt-4o-mini, for each sentence: (i) five paraphrases especially focused on preserving the causal relationship; (ii) one negation that reverses the causal relationship; (iii) two questions that probe understanding of the causal content. considering the original plus paraphrase resulted in 18,366 sentences. since both datasets are used in multiple choice question answering (mcqa) setups, we hereafter refer to the original-based dataset as mcqa and the newer one as mcqa-newer. for instance, the samples generated for the causal type classification task are depicted in figure 1 . examples from the generated dataset can be found in appendix a. ## uncertainty quantification we focus on llms answering multiple-choice questions. let y denote the set of possible options and y the corresponding variable. the probability distribution over the possible choices p (y ) is assumed to be available. we quantify uncertainty through multiple metrics. entropy. we can describe the model uncertainty related to this task by the entropy @xcite , i.e., h := -@xmath2. this is a nonnegative function taking the value of zero for deterministic distributions, and its maximum value for uniform distributions. in the case of quaternary variables, the value of the maximum is 1.39. calibration. we bin predictions by confidence level and compute actual accuracy within each bin. perfect calibration yields a diagonal relationship between confidence and accuracy. we then compute expected calibration error (ece) and adaptive calibration error (ace) @xcite . ece measures how well a model's estimated probabilities match the observed probabilities. a perfectly calibrated model has zero ece. it is com-puted as the weighted average of the absolute differences between average accuracy and average confidence. where, r is the number of bins (typically fixedwidth over the interval [0, 1]), b r is the set of indices of predictions with confidence scores in the r-th bin, n is the total number of samples, to overcome the limitations of ece, such as the bias-variance trade-off induced by binning approaches and its alignment to binary-class settings @xcite , ace was proposed, which utilizes flexible binning @xcite . ace is motivated by the bias-variance trade-off, which suggests that an effective estimate of overall calibration error should emphasize regions where predictions are concentrated, while placing less weight on sparsely populated regions. ace takes as input the predictions p, correct labels, and a number of ranges r: where, r is the number of bins (adaptively chosen so each bin contains roughly the same number of samples), b r , acc(b r ), conf(b r ), and pi are defined as above. consistency. for sentences with multiple paraphrases, we measure whether models make consistent predictions across semantically equivalent inputs. statistical tests. we apply chi-square tests for original versus paraphrase performance, t-tests for dataset comparisons, and anova for differences across causal types. ## experimental design we specifically select models confirmed to be trained on the pile dataset @xcite . since pubmed abstracts used in @xcite are included in the pile, these models necessarily encountered our mcqa sentences {"qa_idx": 0, "context": "however, the small sample size in this study limits its generalizability to diverse populations, so we call for future research that explores ssl-powered personalization at a larger scale.", "text": "however, the small sample size in this study limits its generalizability to diverse populations, so we call for future research that explores ssl-powered personalization at a larger scale.", "text_type": 0, "causal_class_label": 0, "choices": [{"label": 1, "text": "direct causal", "description": "the statement explicitly states that one variable directly causes changes in another."}, {"label": 3, "text": "correlational", "description": "the statement describes an association between variables, but no causation is explicitly stated."}, {"label": 2, "text": "conditional causal", "description": "the statement suggests causation but includes uncertainty through hedging words or modal expressions."}, {"label": 0, "text": "no relationship", "description": "no correlation or causation relationship is mentioned."}, {"label": 4, "text": "other", "description": ""}] figure 1 : examples from the constructed data (task 1) -casual type classification during pretraining. as a control, we also include a model not trained on the pile, allowing us to distinguish data exposure effects from other confounders. in total, we evaluate seven models spanning different architectures, training data, and training approaches. base pretrained models include pythia variants (1.4b, 7b, 12b parameters) and gpt-j-6b, all trained on the pile without instruction tuning. instruction-tuned models include dolly-v2 variants (7b, 12b), which use pythia as base models but undergo additional instruction tuning, and qwen-7b-base, an instruction-tuned model not trained on the pile foot_0 . this selection allows us to isolate the effects of: (i) exposure to training data (the pile), (ii) model scale, and (iii) instruction tuning on causal understanding and uncertainty. to investigate the link between uncertainty sources and familiar causal patterns in the context of causal understanding, we focus on two complementary tasks. task 1: causal type classification. given a sentence @xmath3, where s represents the set of scientific conclusion sentences, the model m must classify the causal relationship into one of four predefined classes: @xmath4, conditional causal, correlational, no relationship}. formally, the model applies a mapping function: this directly tests causal understanding with a random baseline of 1 |y| = 25%. task 2: verbatim memorization probing. following duarte et al. (2024)'s hypothesis that models preferentially select exact text included in their training data (verbatim recall), we test for memorization bias in the context of seenversus-unseen text. given a question q derived from an original sentence s 0 ∈ x original∪n ewer and a set of semantically equivalent paraphrases @xmath5, s 1 , . . . , s n } where meaning(s i ) = meaning(s 0 ) for all i, where meaning(.) indicates the semantics of the sentence, the model must select the most appropriate answer: under the memorization hypothesis, we expect: when s 0 was seen during training. in contrast, without memorization bias, we expect uniform selection probability: p m (s i |q) ≈ 1 |p| for all i. to mitigate documented selection biases (e.g., positional bias) in multiple-choice questions, we randomize the order of answer options for each question. all models are self-hosted and queried via vllm @xcite api with temperature 0.0 for deterministic outputs. for each prediction, we extract: (i) the selected choice, (ii) the probability distribution over choices, (iii) the corresponding entropy, and (iv) the maximum probability (confidence). ## discussion our experimental findings show that uncertainty in causal tasks stems from absent causal understanding rather than insufficient data exposure (figure 5 ). three key insights emerge: first, the independence of performance from training data exposure suggests that simply scaling datasets will not resolve causal understanding deficits. models require architectural innovations or training objectives that explicitly target causal inference. second, overconfidence from instruction tuning poses deployment risks. the shift from calibrated uncertainty (base models: @xmath6.13) to overconfidence (instructiontuned: @xmath7.49) indicates fine-tuning teaches models to suppress appropriate uncertainty. third, the particular difficulty with conditional causal relationships indicates that models lack compositional reasoning about causality. while they may recognize simple cause-and-effect patterns, they fail when conditions, moderators, or exceptions are introduced. these results suggest that causal understanding in llms requires fundamental advances beyond current pretraining paradigms. memorization, even at scale, cannot substitute for genuine causal knowledge. ## conclusion in this work, we focused on analyzing the critical limitations in the causal understanding abilities of large language models (llms). through a controlled evaluation combining causal classification and verbatim memorization probing, we demonstrate that exposure to causal content during pretraining does not guarantee accurate recall or improved causal understanding. our analysis, leveraging multiple uncertainty metrics-including entropy, consistency, calibration, and accuracy-reveals that uncertainty in causal tasks stems primarily from deficits in causal understanding rather than limitations in memorization. addressing these limitations will require a shift beyond current pretraining paradigms-toward models that explicitly encode and reason over causal structures, and that are capable of expressing calibrated uncertainty when faced with ambiguity or unseen conditions.