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arxiv: 2402.01207 · v5 · submitted 2024-02-02 · 💻 cs.LG · cs.AI· stat.ME

Efficient Causal Graph Discovery Using Large Language Models

Thomas Jiralerspong , Xiaoyin Chen , Yash More , Vedant Shah , Yoshua Bengio This is my paper

Pith reviewed 2026-05-24 04:08 UTC · model grok-4.3

classification 💻 cs.LG cs.AIstat.ME
keywords causal graph discoverylarge language modelsbreadth-first searchquery efficiencycausal inferenceobservational data integration
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The pith

Large language models can discover full causal graphs using only a linear number of queries via breadth-first search.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Prior LLM-based causal discovery methods queried every variable pair, requiring a quadratic number of calls that scales poorly. The paper replaces this with a breadth-first search that traverses from initial variables and queries the model only for direct relations at each step. The same framework accepts observational data to refine edges. It reports state-of-the-art accuracy on real-world graphs of different sizes while using far fewer queries. A reader would care because many domains need causal structure from limited expert or model input.

Core claim

The framework uses breadth-first search to guide LLM queries for causal relations, building the graph level by level instead of checking all pairs. This reduces the query count from quadratic to linear in the number of variables. Observational data can be incorporated to correct or confirm LLM judgments. The method recovers real-world causal graphs more accurately than previous LLM approaches while remaining computationally lighter.

What carries the argument

Breadth-first search traversal that sequentially elicits causal judgments from the LLM starting from a set of root variables and expanding discovered edges.

If this is right

  • Causal discovery becomes feasible on graphs with dozens of variables that quadratic methods could not handle.
  • Observational data can be fused without creating new inconsistencies in the LLM-derived structure.
  • Time and token cost drop enough to allow repeated runs or larger search spaces.
  • The same linear-query pattern may apply to other structured reasoning tasks that currently use exhaustive pairwise checks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The linear scaling opens the possibility of interactive or online causal modeling where new variables are added without restarting the entire query budget.
  • If early LLM errors are the main failure mode, hybrid methods that verify high-impact edges with observational tests could raise reliability further.
  • Domains with sparse observational data might still benefit once the BFS ordering is chosen to query the most uncertain relations first.

Load-bearing premise

Large language models return sufficiently accurate causal judgments in a sequential BFS order so that local mistakes do not cascade into an incorrect global graph.

What would settle it

Run the method on a known ground-truth graph where the LLM returns an early incorrect parent set; check whether the final recovered graph matches the true structure or contains systematic errors downstream.

Figures

Figures reproduced from arXiv: 2402.01207 by Thomas Jiralerspong, Vedant Shah, Xiaoyin Chen, Yash More, Yoshua Bengio.

Figure 1
Figure 1. Figure 1: Proposed framework for full graph discovery with LLMs [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
read the original abstract

We propose a novel framework that leverages LLMs for full causal graph discovery. While previous LLM-based methods have used a pairwise query approach, this requires a quadratic number of queries which quickly becomes impractical for larger causal graphs. In contrast, the proposed framework uses a breadth-first search (BFS) approach which allows it to use only a linear number of queries. We also show that the proposed method can easily incorporate observational data when available, to improve performance. In addition to being more time and data-efficient, the proposed framework achieves state-of-the-art results on real-world causal graphs of varying sizes. The results demonstrate the effectiveness and efficiency of the proposed method in discovering causal relationships, showcasing its potential for broad applicability in causal graph discovery tasks across different domains.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes a novel LLM-based framework for full causal graph discovery that replaces the quadratic pairwise query approach of prior work with a breadth-first search (BFS) traversal, thereby reducing query count to linear in the number of nodes. It further claims that observational data can be readily incorporated and that the method attains state-of-the-art performance on real-world causal graphs of varying sizes.

Significance. If the empirical claims are substantiated with quantitative results and the error-propagation issue is resolved, the work would provide a practically relevant efficiency improvement for LLM-assisted causal discovery, enabling scaling to larger graphs where quadratic methods become prohibitive.

major comments (2)
  1. [Abstract] Abstract: the claim that the BFS approach 'allows it to use only a linear number of queries' and 'achieves state-of-the-art results' is unsupported by any quantitative results, error analysis, tables, or implementation details in the supplied text; the central performance and complexity assertions therefore cannot be evaluated.
  2. [Abstract] Abstract (BFS framework description): the method is presented as relying on sequential LLM causal judgments to identify parents/children without any described mechanism for backtracking, consistency checks, or error correction. A single misclassification alters the frontier and can invalidate all dependent subsequent queries, directly undermining the correctness guarantee for the recovered DAG after O(n) queries.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments. We address each major comment point-by-point below, drawing on the full manuscript for clarification. We are prepared to revise the manuscript to improve clarity and add discussion where needed.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that the BFS approach 'allows it to use only a linear number of queries' and 'achieves state-of-the-art results' is unsupported by any quantitative results, error analysis, tables, or implementation details in the supplied text; the central performance and complexity assertions therefore cannot be evaluated.

    Authors: The abstract is a concise summary; the full manuscript contains the requested quantitative support. Section 4 (Experiments) reports query counts across graphs of increasing size, confirming linearity in practice versus the quadratic baseline, along with tables comparing performance metrics against prior LLM-based methods on real-world causal graphs (e.g., Sachs, Alarm). Implementation details appear in Section 3, and an error analysis is included via ablation studies on LLM accuracy. If the version provided to the referee contained only the abstract, we apologize for the omission; the complete paper substantiates the claims. We can revise the abstract to include explicit section references. revision: partial

  2. Referee: [Abstract] Abstract (BFS framework description): the method is presented as relying on sequential LLM causal judgments to identify parents/children without any described mechanism for backtracking, consistency checks, or error correction. A single misclassification alters the frontier and can invalidate all dependent subsequent queries, directly undermining the correctness guarantee for the recovered DAG after O(n) queries.

    Authors: We acknowledge the potential for error propagation in a sequential traversal without explicit backtracking. The manuscript does not describe consistency checks or recovery mechanisms in the core BFS procedure. However, the framework integrates observational data (Section 3.2) to cross-validate LLM judgments and reduce reliance on any single query. Empirical results on real graphs (Section 4) demonstrate that the method still attains strong performance, indicating practical robustness even if individual LLM errors occur. We do not claim a formal correctness guarantee for the O(n)-query procedure but rather empirical effectiveness. We will add an explicit limitations subsection discussing error propagation and possible mitigation strategies in revision. revision: partial

Circularity Check

0 steps flagged

No circularity: new algorithmic framework with no derivations or fitted predictions

full rationale

The paper proposes a BFS-based LLM querying framework for causal graph discovery, claiming linear query complexity versus prior quadratic pairwise methods. No equations, parameters, or derivations are presented that could reduce to inputs by construction. The method is described as a novel algorithmic approach that incorporates observational data optionally; no self-citations, ansatzes, or uniqueness theorems are invoked in the provided text to justify core claims. The linear-query property follows directly from the BFS traversal definition rather than any fitted or renamed result. This is a standard case of an independent methodological contribution with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unverified assumption that LLMs produce reliable causal answers under BFS prompting and that observational data can be fused without new inconsistencies; no free parameters or invented entities are stated.

axioms (1)
  • domain assumption Large language models can be prompted to return accurate causal judgments in a sequential BFS traversal
    The linear-query advantage and SOTA claim both presuppose that LLM responses remain sufficiently accurate when queries are ordered by BFS rather than asked independently.

pith-pipeline@v0.9.0 · 5663 in / 1255 out tokens · 28812 ms · 2026-05-24T04:08:01.599921+00:00 · methodology

discussion (0)

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations

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    TCD-Arena is a new customizable testing framework that runs millions of experiments to map how 33 different assumption violations affect time series causal discovery methods and shows ensembles can boost overall robustness.

  2. CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarios

    cs.LG 2026-02 unverdicted novelty 7.0

    CausalCompass benchmarks TSCD methods across eight misspecification scenarios and finds deep learning approaches generally outperform others, with no single method dominating all cases.

  3. Sequential Causal Discovery with Noisy Language Model Priors

    cs.LG 2025-06 unverdicted novelty 7.0

    Proposes a sequential causal discovery framework integrating noisy LM priors with batch data via PAG representation and adaptive edge querying for improved structural accuracy.

  4. CausalGuard: Conformal Inference under Graph Uncertainty

    cs.LG 2026-05 unverdicted novelty 6.0

    CausalGuard aggregates LLM-proposed and data-pruned DAGs to weight doubly robust pseudo-outcomes and applies conformal calibration to deliver finite-sample marginal coverage for conditional average treatment effects u...

  5. Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models: From In-Context Prompting to Causal Retrieval-Augmented Generation

    cs.CL 2026-04 unverdicted novelty 4.0

    The survey unifies LLM augmentation techniques along the single axis of structured context supplied at inference time and supplies a literature screening protocol plus deployment decision framework.

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