{"id":"3b445fb7-994e-468d-9308-076c22270fd6","arxiv_id":"2505.18034","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":3.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"Guiding Qwen3-32B to construct an intermediate knowledge graph before answering causal queries raises Corr2Cause F1 from 32.71 to 48.26.","lead":"This paper tests whether making a large language model build a knowledge graph before answering causal questions improves its accuracy. On a benchmark called Corr2Cause, a Qwen3 model's F1 score rose from about 33 to 48, but the evaluation lacks error bars, a comparison to an existing structured-prompting method, and released code.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The reported gain is confounded: the structured arm adds reasoning tokens and tool-call scaffolding, and the elicited undirected skeleton cannot by itself support directed causal claims; no ablation isolates graph content.","rationale":"The reader's conditional verdict is appropriate; my concern makes the required condition more specific. The paper's headline claim is not just that the pipeline works, but that 'structured thinking'—the intermediate knowledge graph—is responsible for the improvement. The current experiment cannot establish that because the structured arm differs from the baseline in several dimensions: extra tokens, tool-call decoding, graph schema, and syntactic DOT representation. The internal contradiction in §3.1 (directions unspecified versus explicitly defined) underscores that the method has not specified how a directed causal conclusion is derived from an undirected skeleton. A random-graph condition and a chain-of-thought control would settle whether the graph's content is load-bearing or merely an incidental scaffold for additional reasoning. I therefore keep the reader's CONDITIONAL verdict, with the added condition that an ablation isolating graph content be run before the claim is accepted as established.","tokens_in":7845,"tokens_out":5508,"duration_ms":50854,"concrete_test":"Using the same test subset and Qwen3-32B, run three conditions: (A) direct baseline; (B) a chain-of-thought prompt ('reason step by step, then answer Yes/No') with output length matched to the KG pipeline but no graph schema; (C) the full pipeline with the graph-generation stage replaced by a fixed random graph over the same variables, or by the true dependency skeleton from the Corr2Cause generator if available. If (B) matches (C) or the reported 48.26 F1, the improvement is not attributable to graph content; if random-graph (C) retains the full-pipeline F1, the model is ignoring the graph. Also report the filtered-subset size and filter rule from Algorithm 1, line 3.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central comparison is confounded. The baseline is a one-turn 'Just answer Yes or No' prompt, while the structured condition receives a multi-stage pipeline: graph-generation prompt, regex-constrained tool-call decoding, DOT serialization, and a second query with the graph in context. Any of these extra components—not just the graph's semantic content—could produce the F1 gain. The paper claims advantages over chain-of-thought (§6) but reports no CoT or token-matched reasoning baseline. Compounding this, §3.1 constructs an undirected skeleton whose labels are 'correlates with' / 'independent of' and states that 'causal directions remain unspecified', then immediately claims 'causal directions are explicitly defined'. §3.3 asks the model to judge directed hypotheses such as 'X directly causes Z' against this skeleton. A dependency skeleton generally does not identify direct causes without additional assumptions, such as orientation rules from v-structures or conditional independencies, and the paper supplies no identifiability argument for the Corr2Cause instances or for the particular graph the model is prompted to emit. The reported gain may therefore reflect extra deliberation or scratchpad effects, not the structured graph itself.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a structured causal-inference prompting pipeline: before asking an LLM to judge a causal hypothesis, the model is prompted to generate a knowledge graph (an undirected dependency skeleton) from the correlational premises, using a regex-constrained tool-calling format; the graph is then serialized and re-fed to the model together with the original query for the final yes/no answer. Experiments on the Corr2Cause benchmark with Qwen3-32B report an F1 improvement from 32.71 (direct zero-shot prompting) to 48.26 with the structured approach, along with a recall gain from 33.89 to 65.56. The paper also includes a small-scale study of three graph serialization styles on Qwen3-4B and compares the structured result against GPT-4 and BART-MNLI numbers from the literature.","tokens_in":8068,"tokens_out":4684,"duration_ms":40580,"significance":"The central idea -- interposing an explicit, machine-validated graph-construction step between the correlational evidence and the causal judgment -- is interesting and, if the empirical claim is sound, would be a useful practical advance for zero-shot causal inference with LLMs. The paper is clearly written and the pipeline is described in enough detail to be reproducible in principle. However, the current experimental evidence does not yet establish that the structured graph itself causes the reported improvement: the structured arm differs from the baseline in many components (extra reasoning tokens, tool-call decoding, DOT serialization, a second query), the knowledge graph is an undirected skeleton that is not shown to identify directed causal claims, the graph serialization style is selected on the test set, no variance or significance information is reported, and the most relevant prior baseline (PC-SubQ) is not compared. The contribution is therefore plausible but not yet firmly supported, and the missing experiments are well within the scope of a revision.","major_comments":[{"comment":"The headline 15.55-point F1 gain is measured against a one-turn 'Just answer Yes or No' prompt, while the structured condition adds several components: a graph-generation prompt, regex-constrained tool-call decoding, DOT serialization, and a second query with the graph in context. Any of these, not just the graph content, could explain the gain. The paper should include ablations that control for the extra computation and structure: for example, a two-pass baseline that repeats the question before answering, a control where the intermediate is a semantically irrelevant but equally structured object (e.g., a checklist of the premises), and an ablated structured condition where the graph edges are randomly shuffled or replaced by empty relations. Without such controls, the central claim that knowledge-graph construction is the source of the improvement is not established.","section":"§5.2, Table 3"},{"comment":"There is an internal inconsistency in the status of the graph: Section 3.1 says 'causal directions remain unspecified' and the graph labels are only 'correlates with' or 'independent of,' while Section 3.3 states that 'causal directions are explicitly defined' and asks the model to judge directed hypotheses like 'X directly causes Z.' An undirected dependency skeleton does not generally determine direct causal relationships without additional orientation assumptions (e.g., v-structure rules), and the paper supplies no identifiability argument for the Corr2Cause instances. The observed gain may therefore come from the model being primed to reason stepwise rather than from the graph's causal content. The authors should either provide a formal argument (or dataset statistics) showing the skeleton is sufficient for the Corr2Cause instances, or include an ablation in which the graph is replaced by a structurally identical but semantically vacuous graph.","section":"§3.1 and §3.3"},{"comment":"The KG serialization style (Style 2, 'a -> b + b -> a') is selected by comparing F1 on the Corr2Cause test set (or a filtered subset of it) with Qwen3-4B, and the same style is then adopted for the main results on the same benchmark in Table 3. This is a form of test-set tuning, which can inflate the reported F1 of 48.26 and makes the main result hard to interpret as a fixed evaluation. The style selection should be performed on a separate validation set (e.g., a held-out split from the Corr2Cause training portion) and the main evaluation should report the result with the style chosen a priori, or otherwise demonstrate that the style choice does not materially affect the reported comparison.","section":"§5.1, Table 1"},{"comment":"The paper does not report the number of examples in the test subset, the number of instances used for the style-selection experiment, or any variance, confidence intervals, or significance tests. The differences between KG styles (e.g., F1 of 0.3315 vs. 0.3617) are small and could be within sampling noise, and the main 32.71-to-48.26 comparison could be driven by a small set of positive examples. The authors should report N for each experiment and include a paired significance test (e.g., McNemar's test) for the head-to-head comparisons in Table 3.","section":"§5, Tables 1-3"},{"comment":"The related work explicitly describes PC-SubQ [Sgouritsa et al., 2024] as a multi-step prompting strategy that 'significantly improves performance across several LLMs' on Corr2Cause, yet the experimental section does not compare the proposed method against PC-SubQ or any other structured-prompting baseline. Since PC-SubQ is the most directly relevant existing approach, its omission weakens the claim in Section 6 that the graph-based pipeline is superior to 'chain-of-thought strategies.' The main comparison should include a PC-SubQ baseline implemented with the same Qwen3-32B model.","section":"§2 and §5"},{"comment":"The conclusion states that 'the graph-guided method maintains its accuracy and recall even when variable names or textual descriptions are paraphrased or replaced,' but no experiment supporting this robustness claim appears anywhere in the manuscript. The robustness assertion is load-bearing for the paper's broader generalization claim. Either add the corresponding experiments (for instance, using the out-of-distribution perturbations described in Jin et al. [2024]) or remove the claim from the conclusions.","section":"§6"}],"minor_comments":[{"comment":"The phrase 'Qwen3-32B model (reasoning model)' is not a standard naming convention; please clarify that Qwen3 is a model family with a reasoning mode that supports tool calling.","section":"Abstract and §1"},{"comment":"The reference 'Kalisch and Bühlman' and the corresponding bibliography entry should be corrected to 'Bühlmann'.","section":"§2"},{"comment":"Step 4, 'Split xi into Premise and Hypothesis,' is underspecified; the paper should state how the split is performed (e.g., given the dataset format) so that the preprocessing is reproducible.","section":"Algorithm 1"},{"comment":"The regex-constrained decoding components (convert_json_schema_to_str, build_regex_from_schema, RegexLogitsProcessor) are named but not defined; a short pseudocode block or a reference to the implementation would improve reproducibility.","section":"§3.2"},{"comment":"Figure 1 is referenced in Section 3.1 but does not appear in the manuscript text; please include the figure or remove the reference.","section":"Figure 1"},{"comment":"The text reports 'F1, precision, and recall' while the table columns are ordered Accuracy, Precision, Recall, F1; please align the ordering for readability.","section":"§5.1, Table 1"}],"recommendation":"major_revision","confidential_remarks":"This is a straightforward empirical paper with a timely idea. The main risk is that the current evidence does not isolate the effect of the knowledge graph from the many other differences between the structured and baseline conditions, and the test-set-based style selection and missing PC-SubQ baseline further weaken the headline claim. These are fixable within the manuscript's scope, so I recommend major revision rather than rejection. There are no ethical or provenance concerns; the paper is transparent about its dependence on a specific model and tool-calling setup."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Two things you should know. First, the paper gives a concrete, clearly specified recipe: force the LLM to emit a JSON knowledge graph from the correlational premises before answering a causal query. Second, the headline F1 gain (32.71 to 48.26 on Corr2Cause with Qwen3-32B) is plausible but not established; the comparison is confounded and the evaluation lacks basic statistical support.\n\nWhat's new is modest but real: the combination of regex-constrained tool-calling and JSON graph serialization applied to Corr2Cause. It's a close variant of PC-SubQ, which the paper cites, and it adds engineering detail but not a new causal idea. The writing is clear, the pipeline is described in enough detail to reproduce, and the direct-vs-structured comparison is internally consistent. That's genuine credit.\n\nThe soft spots are substantial. The structured arm adds tool-call scaffolding, extra generated tokens, and a second query, so you can't attribute the F1 gain to the graph's content. There's no CoT or token-matched baseline, no PC-SubQ comparison, no sample size, no variance, no code. The KG style was selected on the same test benchmark (Section 5.1 feeds Section 5.2), so the main result is partly a post-hoc selection. The robustness claim in the abstract and Section 6 is asserted without any supporting experiment. And there's a real conceptual gap: Section 3.1 says causal directions remain unspecified, then Section 3.3 asks directed causal questions against an undirected skeleton whose edges are only 'correlates with' or 'independent of'. A dependency skeleton generally doesn't identify direct causes without orientation rules or other assumptions, and the paper supplies no identifiability argument for these instances. So the structure may be an incidental scaffold rather than the source of the improvement.\n\nWho is this for? People working on prompt engineering for LLM causal reasoning. It's a useful recipe to try. But the paper needs major revision before the claim is trustworthy. I'd send it to peer review rather than desk-reject, because the idea is worth testing and the method is described well enough to be evaluated. But a referee should push hard for baselines, statistics, code, and an identifiability argument.","headline":"A concrete prompt-engineering recipe for LLM causal reasoning, but the evaluation is too confounded to support the headline F1 claim.","tokens_in":8562,"tokens_out":2866,"would_cite":false,"duration_ms":21037,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Forcing an LLM to build a knowledge graph before answering causal queries lifts Corr2Cause F1 from 32.71 to 48.26 in a zero-shot setting.","keywords":["causal inference","large language models","Corr2Cause","structured reasoning","knowledge graph prompting","tool calling","zero-shot generalization","correlation vs causation"],"falsifier":"Compare the two-stage pipeline against a control where the first stage outputs a plain bulleted list of the same premises instead of a JSON graph, with everything else held fixed. If F1 stays near 48.26, the structure of the graph is not carrying the result; if F1 falls back toward 32.71, the graph format itself is responsible.","tokens_in":1724,"feed_emoji":"🧠","tokens_out":2054,"duration_ms":66168,"temperature":0.7,"pith_summary":"The paper argues that large language models fail at causal inference because they answer from surface textual patterns, and that making the model build an explicit knowledge graph from the given correlational statements before it judges a causal claim fixes much of that failure. On the test subset of the Corr2Cause benchmark, Qwen3-32B improves from F1 32.71 under direct zero-shot prompting to 48.26 under the graph-first pipeline, a relative gain of 47.5 percent driven mainly by recall rising from 33.89 to 65.56. The authors present this as a no-fine-tuning path to better causal reasoning, with gains also in precision and with accuracy essentially unchanged. A sympathetic reader should care because the result suggests that how an LLM is prompted to structure its intermediate reasoning can matter more than model scaling for causal generalization.","feed_headline":"LLM causal F1 jumps from 32.7 to 48.3 with graph-first prompting","feed_subtitle":"No fine-tuning is needed: asking Qwen3-32B to encode premises as a knowledge graph before answering nearly doubles recall.","key_machinery":"The load-bearing object is an intermediate knowledge-graph skeleton produced by the LLM itself. A first prompt instructs the model to encode the premise's correlations and independencies as nodes and undirected edges in a strict JSON schema; a regex logits processor forces every output to conform exactly to that schema. A second prompt supplies the serialized graph (in a two directed edges per undirected link DOT notation, which the authors find best for model comprehension) and asks the model to test the causal hypothesis against it. The intended division of labor is that the first stage distills the textual premise into a formal structure and the second stage becomes a straightforward graph-consistency check, decoupling correlation analysis from causal judgment.","core_discovery":"The paper's central claim is that an LLM can generalize from correlation to causation far better when it is first guided to construct a structured knowledge graph representing the correlational premises, and only then asked whether a hypothesized causal relation holds. The graph is built through a tool-calling step with regex-constrained JSON output, so the model produces a machine-readable skeleton whose edges are labelled only as correlates with or independent of; causal directions are left unspecified at construction. A second prompt gives the graph back to the model and asks it to verify the causal hypothesis against that structure. On Corr2Cause, this two-stage pipeline lifts Qwen3-32B's F1 from 32.71 to 48.26, with recall nearly doubling, and the authors report that the gain is stable under paraphrased and variable-renamed queries, which they read as evidence of generalization rather than memorized patterns.","pith_inferences":["Editorial inference: because the skeleton stores only correlations and independencies and no causal orientation, the F1 gain could come from forcing the model to decompose the problem into a structured listing, not from the graph's causal content; an ablation replacing the JSON graph with a plain structured list would separate the two.","Editorial inference: if the gain is genuinely structural, the same two-stage recipe may transfer to other tasks that require checking consistency against many premises, such as legal or medical reasoning, where a schema-constrained intermediate representation is natural.","Editorial inference: the paper's reported failure mode of graph-generation errors cascading into wrong judgments suggests a testable extension: feed the model a hand-corrected graph and measure how much of the residual error is graph error versus inference error.","Editorial inference: a symbolic checker could certify the generated skeleton against the premise before the second stage runs; if F1 improves further with certified graphs, that would confirm the representation is load-bearing."],"forward_implications":["Zero-shot causal inference on Corr2Cause can be improved by about 47.5 percent in F1 without fine-tuning, simply by inserting a graph-construction step into the prompt.","The gain is driven by recall: the graph-first model finds many more true causal relations (recall rising from 33.89 to 65.56) while keeping accuracy flat, reducing the failure mode induced by the benchmark's 80/20 class imbalance.","The effect is not model-agnostic: Qwen3-32B, a model with tool-calling and structured-reasoning ability, benefits strongly, while Qwen2.5-32B gains only modestly, indicating that model capability and prompting structure interact.","The intermediate graph makes the model's reasoning inspectable: the JSON can be read and validated before the final answer, supporting consistency checks that direct prompting does not allow.","The method transfers, at least within the paper's tests, to paraphrased or variable-renamed queries, suggesting robustness to distribution shift."],"supporting_citations":[{"why":"This work introduces the Corr2Cause benchmark and reports the GPT-4 and fine-tuned baseline scores that the paper's structured method is designed to beat.","marker":"[Jin et al., 2024]"},{"why":"This work proposes PC-SubQ, a multi-step prompting baseline aligned with the PC algorithm, which the graph-first pipeline is compared against as an alternative structured-prompting route.","marker":"[Sgouritsa et al., 2024]"},{"why":"This report documents the Qwen3 model's tool-calling and structured reasoning capabilities that the method depends on for building the knowledge graph.","marker":"[QwenLM, 2025]"},{"why":"This foundational reference supplies the PC algorithm and DAG-based causal discovery framework that motivates the intermediate graph representation.","marker":"[Kalisch and Bühlman, 2007]"},{"why":"This paper defines chain-of-thought prompting, the standard reasoning baseline from which the graph-based pipeline is explicitly distinguished.","marker":"[Wei et al., 2022]"},{"why":"This work introduces tool-augmented language models, motivating the design of routing LLM reasoning through explicit tool-call outputs.","marker":"[Parisi et al., 2022]"},{"why":"This work documents LLM unreliability in distinguishing causation from correlation, which is the problem the paper addresses.","marker":"[Kiciman et al., 2024]"}],"fun_headline_variants":["Graph-first prompting boosts LLM causal F1 by 47.5%","No fine-tuning: knowledge graph step lifts LLM causal accuracy","Structured thinking via knowledge graph improves causal inference in LLMs","LLM causal F1 32.7→48.3 with structured knowledge graph prompting","Encouraging LLMs to build graphs before causal answers nearly doubles recall"],"cache_read_input_tokens":10752,"weakest_assumption_plain":"The load-bearing premise is that a graph recording only which variables correlate and which are independent gives the model enough information to answer a directed causal question, but correlation and independence alone do not determine causation, so the graph may simply be a useful scaffold rather than the true source of the improvement.","fun_headline_variants_meta":{"raw":{"variants":["Graph-first prompting boosts LLM causal F1 by 47.5%","No fine-tuning: knowledge graph step lifts LLM causal accuracy","Structured thinking via knowledge graph improves causal inference in LLMs","LLM causal F1 32.7→48.3 with structured knowledge graph prompting","Encouraging LLMs to build graphs before causal answers nearly doubles recall"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000294,"raw_usage":{"total_tokens":1711,"prompt_tokens":947,"completion_tokens":764,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":563,"completion_tokens_details":{"reasoning_tokens":681}},"tokens_in":563,"tokens_out":764,"duration_ms":6325,"temperature":1.0,"reasoning_tokens":681,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T14:35:33.953094+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Compare the two-stage pipeline against a control where the first stage outputs a plain bulleted list of the same premises instead of a JSON graph, with everything else held fixed. If F1 stays near 48.26, the structure of the graph is not carrying the result; if F1 falls back toward 32.71, the graph format itself is responsible.","supporting_citations":[{"cited_title":"Diab, and Bernhard Sch \\\" o lkopf","cited_arxiv_id":null,"evidence_quote":"This work introduces the Corr2Cause benchmark and reports the GPT-4 and fine-tuned baseline scores that the paper's structured method is designed to beat."},{"cited_title":"Qwen3 Technical Report","cited_arxiv_id":null,"evidence_quote":"This report documents the Qwen3 model's tool-calling and structured reasoning capabilities that the method depends on for building the knowledge graph."},{"cited_title":"Causal reasoning and large language models: Opening a new frontier for causality","cited_arxiv_id":null,"evidence_quote":"This work documents LLM unreliability in distinguishing causation from correlation, which is the problem the paper addresses."}],"review_version":1}