Enhancing Causal Reasoning in Large Language Models: A Causal Attribution Model for Precision Fine-Tuning

Hengrui Cai; Rui Song; Shengjie Liu

arxiv: 2401.00139 · v3 · pith:FLWWCISPnew · submitted 2023-12-30 · 💻 cs.AI · cs.CL· cs.LG· stat.ME

Enhancing Causal Reasoning in Large Language Models: A Causal Attribution Model for Precision Fine-Tuning

Hengrui Cai , Shengjie Liu , Rui Song This is my paper

Pith reviewed 2026-05-24 04:54 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.LGstat.ME

keywords causal reasoninglarge language modelscausal attributionfine-tuningcausal discoverydo-operatorsinterpretability

0 comments

The pith

A causal attribution model built with do-operators identifies which LLM components drive causal reasoning and guides targeted fine-tuning.

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

The paper introduces a causal attribution model that applies do-operators to build interventional scenarios inside large language models. These scenarios produce scores that quantify how much each model component contributes to causal reasoning. Testing the scores on causal discovery tasks across domains shows that LLMs succeed mainly when given context and domain knowledge, while numerical data is used only for limited correlation calculations rather than true causation. The findings directly motivate a fine-tuned LLM specialized for pairwise causal discovery that combines both knowledge and numerical signals.

Core claim

The central claim is that do-operator interventions inside LLMs generate attribution scores that systematically measure component contributions to causal reasoning; evaluations reveal LLMs' heavy dependence on context and domain knowledge with only correlational use of numbers, which then justifies a precision fine-tuned model that correctly integrates both sources for pairwise causal discovery.

What carries the argument

The causal attribution model, which uses do-operators to construct interventional scenarios and derive scores quantifying each component's contribution to the causal reasoning process.

If this is right

Precision fine-tuning guided by the attribution scores produces an LLM that leverages both domain knowledge and numerical data for pairwise causal discovery.
LLM effectiveness on causal tasks improves when fine-tuning targets components shown by the scores to carry the reasoning load.
The same attribution approach can be applied across multiple domains to diagnose reliance on context versus data.
Numerical data alone is insufficient for causal discovery in current LLMs and must be paired with explicit knowledge.

Where Pith is reading between the lines

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

The attribution technique could be tested on non-causal reasoning tasks to check whether the same component contributions appear.
If the scores prove stable across model sizes, they might serve as a diagnostic tool for selecting which LLMs to fine-tune for specific causal problems.
Extending the interventions to measure interactions between components could reveal whether knowledge and numerical pathways operate independently or jointly.

Load-bearing premise

The performance differences observed after applying do-operators inside the LLM accurately reflect causal contributions of specific components rather than mere correlations with overall task success.

What would settle it

If ablating components ranked highest by the attribution scores produces no greater drop in causal discovery accuracy than ablating randomly chosen components, the attribution model does not isolate causal contributions.

Figures

Figures reproduced from arXiv: 2401.00139 by Hengrui Cai, Rui Song, Shengjie Liu.

**Figure 1.** Figure 1: Ability attribution on answering the causal question by generating counterfactual examples. contributions of our research are threefold. • We develop a causal attribution model and propose the definitions of marginal and conditional attributions of knowledge and data, through the notion of “do-operators” (Pearl et al., 2009). The proposed definitions differentiate and quantify the effects of omitted knowl… view at source ↗

**Figure 2.** Figure 2: Experiment design for LLMs’ answering causal questions with encouraging prompts. 3.5 Ability Attribution: Random Guess To set the baseline for LLMs’ performance without input data and knowledge, we design an experiment of random guess to omit both data and knowledge inputs. This approach estimates P (ybi = yi | do (ki = ∅, di = ∅), ci , ui) in Definitions 2.3-2.4, where LLMs must operate without informativ… view at source ↗

**Figure 3.** Figure 3: Illustration of the experiment design of the pairwise causal discovery task. To illustrate, in the Galton Family dataset, we simulate Father’s Height data by Father’s Height := f(Child’s Height) + U, where f is a linear function and U is non-Gaussian noise, such as chi-squared noise. The evaluation is conducted under three scenarios: (1) employing LinGAM (Shimizu et al., 2011) directly on simulated data as… view at source ↗

**Figure 4.** Figure 4: Illustration of the reverse causal discovery task. additional experiment with reversed causal pairs, as in [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗

**Figure 5.** Figure 5: Conversely, the relatively low marginal attribution of data (MAD) and conditional [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗

**Figure 5.** Figure 5: The differences between attribution scores (MAK-MAD) among LLMs to demonstrate a hierarchy in their knowledge depth [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗

**Figure 6.** Figure 6: Performance of GPT-4 turbo under Raw Data versus Reverse-Raw. performance differences, these models exhibit consistent behavior across datasets: if one model performs poorly on a dataset, others also tend to perform poorly on the same dataset. For SHD (Figure E.2 in Appendix), GPT-4 Turbo achieves the lowest or comparable SHD across the datasets. GPT-3.5 performs slightly worse than Claude 2 but is compara… view at source ↗

**Figure 7.** Figure 7: Illustration of the instruction of the generated dataset for fine-tuning LLMs. the LLM conducts a correlation analysis first and then outputs the causal relation based on the correlation, without performing a conditional independence test. Moreover, the result from the correlation analysis is often incorrect (see Figure F.1 for the CoT of LLMs in causal discovery). Specifically, the correct causal pair is … view at source ↗

read the original abstract

This paper introduces a causal attribution model to enhance the interpretability of large language models (LLMs) and improve their causal reasoning abilities via precise fine-tuning. Despite LLMs' proficiency in diverse tasks, their reasoning processes often remain black box, and thus restrict targeted enhancement. We propose a novel causal attribution model that utilizes "do-operators" for constructing interventional scenarios, allowing us to quantify the contribution of different components in LLMs's causal reasoning process systematically. By assessing the proposed attribution scores through causal discovery tasks across various domains, we demonstrate that LLMs' effectiveness in causal discovery heavily relies on provided context and domain-specific knowledge but can also utilize numerical data with limited calculations in correlation, not causation. This motivates the proposed fine-tuned LLM for pairwise causal discovery, effectively and correctly leveraging both knowledge and numerical information.

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.

Desk Editor's Note private letter to a colleague

The paper applies do-operators inside LLMs for attribution scores without an explicit SCM, so the scores likely rank performance changes rather than isolate causal effects.

read the letter

The paper's core move is to define a causal attribution model that uses do-operators to build interventional scenarios inside LLMs, score component contributions to causal reasoning, and then apply those scores for precision fine-tuning on pairwise causal discovery tasks. The abstract reports that this reveals LLMs depend heavily on context and domain knowledge while treating numerical data mostly as correlation rather than causation, which then motivates the fine-tuned model that supposedly uses both sources correctly. That framing and the reported experiments across domains are the main new element; prior work on causal tools for LLMs exists, but this specific attribution-to-fine-tuning pipeline is not obviously duplicated in the cited literature. The experiments appear to supply a concrete demonstration that current models fall short on pure numerical causal discovery, which is useful to see. The central weakness is exactly the stress-test concern: no structural causal model is defined for the LLM's internal states, so the interventions (via prompting or activation edits) cannot block back-door paths or isolate direct effects. The resulting scores therefore measure observed output shifts, not causal contributions. The abstract supplies no equations, metrics, error bars, or implementation details to check whether the fine-tuning actually overcomes this. This is a load-bearing gap for the claim that the model 'effectively and correctly' leverages both knowledge and numerical information. The work is aimed at researchers trying to make LLM causal reasoning more interpretable and targeted. Readers already working on causal inference for AI systems would get value from the task results and the proposed pipeline, even if they have to re-derive the attribution step themselves. It deserves a serious referee to examine the full methods and any validation data.

Referee Report

2 major / 2 minor

Summary. The paper introduces a causal attribution model that applies do-operators to construct interventional scenarios inside LLMs, thereby quantifying the contribution of different model components to causal reasoning. It evaluates the resulting attribution scores on causal discovery tasks across domains, concluding that LLMs rely primarily on provided context and domain-specific knowledge (with only limited use of numerical data for correlations, not causation). This observation motivates a precision fine-tuned LLM specialized for pairwise causal discovery that is claimed to correctly leverage both knowledge and numerical information.

Significance. If the attribution scores can be shown to isolate genuine causal contributions rather than performance correlations, the approach would offer a concrete mechanism for targeted interpretability and improvement of causal reasoning in LLMs. The empirical finding that LLMs treat numerical data associatively rather than causally is a useful diagnostic observation that could guide future prompting and fine-tuning strategies.

major comments (2)

[Abstract / method] Abstract and method description: the central construction applies do-operators to produce attribution scores that are asserted to quantify causal contributions of LLM components. No explicit structural causal model (SCM) is supplied in which the interventions correspond to do(·) operations that block back-door paths. Interventions appear to be realized via prompting or activation edits whose semantics remain associative; consequently the scores may rank components by observed performance delta rather than by causal effect. This premise is load-bearing for both the attribution model and the subsequent fine-tuning claim.
[Abstract] Abstract: the claim that the fine-tuned model 'effectively and correctly' leverages both knowledge and numerical information rests on the attribution scores, yet the abstract supplies no equations, validation metrics, error bars, or implementation details for either the attribution procedure or the fine-tuning objective. Without these, the central demonstration cannot be assessed.

minor comments (2)

[Abstract] The abstract states results but contains no quantitative metrics or task definitions; a methods or results section should supply the precise causal discovery tasks, domains, and performance numbers used to support the reliance-on-context conclusion.
[method] Notation for the attribution scores and the precise definition of the interventional scenarios should be introduced with equations rather than prose only.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the opportunity to respond to the referee's report. We address each major comment point by point below, indicating where revisions will be incorporated.

read point-by-point responses

Referee: [Abstract / method] Abstract and method description: the central construction applies do-operators to produce attribution scores that are asserted to quantify causal contributions of LLM components. No explicit structural causal model (SCM) is supplied in which the interventions correspond to do(·) operations that block back-door paths. Interventions appear to be realized via prompting or activation edits whose semantics remain associative; consequently the scores may rank components by observed performance delta rather than by causal effect. This premise is load-bearing for both the attribution model and the subsequent fine-tuning claim.

Authors: We thank the referee for this substantive observation. Our construction applies the do-operator to describe interventions realized through prompting and activation edits within the LLM, without supplying an explicit SCM that formally blocks back-door paths. The resulting attribution scores are computed from performance deltas under these interventions and therefore reflect interventional rather than strictly causal effects in the SCM sense. We will revise the method section to explicitly state this distinction, reframe the attribution scores as interventional contributions, and adjust the language around the fine-tuning motivation to avoid overclaiming causal isolation. These changes will be reflected in the next manuscript version. revision: yes
Referee: [Abstract] Abstract: the claim that the fine-tuned model 'effectively and correctly' leverages both knowledge and numerical information rests on the attribution scores, yet the abstract supplies no equations, validation metrics, error bars, or implementation details for either the attribution procedure or the fine-tuning objective. Without these, the central demonstration cannot be assessed.

Authors: The abstract is intentionally concise and provides only a high-level summary. The equations defining the attribution procedure, the validation metrics with error bars, and the fine-tuning objective and implementation details appear in Sections 3 and 4 of the manuscript. We will make a partial revision by adding a brief pointer in the abstract to these sections so readers can locate the supporting details more readily. revision: partial

Circularity Check

0 steps flagged

No circularity: novel attribution model proposed without reduction to inputs

full rationale

The paper proposes a new causal attribution model that applies do-operators to construct interventional scenarios for quantifying component contributions in LLMs. No equations appear in the provided abstract or description, and no self-citations or fitted parameters are invoked as load-bearing premises for the central construction. Assessments occur via separate empirical causal discovery tasks across domains, providing independent evaluation rather than deriving results by construction from the model's own definitions. The derivation chain remains self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Only the abstract is available, so the ledger is necessarily incomplete and limited to elements stated or implied at that level.

axioms (1)

domain assumption Do-operators can be applied inside LLMs to construct interventional scenarios that isolate component contributions to causal reasoning
This is the foundational modeling choice stated in the abstract for the causal attribution model.

invented entities (1)

Causal attribution model no independent evidence
purpose: To quantify the contribution of different LLM components to causal reasoning via do-operators
New construct introduced in the abstract; no independent evidence outside the paper is provided.

pith-pipeline@v0.9.0 · 5676 in / 1368 out tokens · 19716 ms · 2026-05-24T04:54:13.596151+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We propose a novel causal attribution model that utilizes 'do-operators' for constructing interventional scenarios, allowing us to quantify the contribution of different components in LLMs's causal reasoning process systematically.
IndisputableMonolith/Foundation/ArrowOfTime.lean forward_accumulates unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Definition 2.1. Conditional Attribution of Knowledge (CAK) Given Data: CAKi = P (byi = yi | do (ki = ki, di = di) , ci, ui) − P (byi = yi | do (ki = ∅, di = di) , ci, ui)

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

8 extracted references · 8 canonical work pages · 2 internal anchors

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Shimizu, S., Hoyer, P. O., Hyv¨ arinen, A. and Kerminen, A. (2006 b), ‘A linear non-gaussian acyclic model for causal discovery’, Journal of Machine Learning Research 7, 2003–2030. URL: http://jmlr.org/papers/v7/shimizu06a.html Shimizu, S., Inazumi, T., Sogawa, Y., Hyv¨ arinen, A., Kawahara, Y., Washio, T., Hoyer, P. O. and Bollen, K. (2011), ‘Directlinga...

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Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D. et al. (2022), ‘Chain-of-thought prompting elicits reasoning in large language models’, Advances in Neural Information Processing Systems 35, 24824–24837. Weidinger, L., Kamm, L., Mendelsohn, J., Cotterell, R., Riedel, S. and Hovy, D. (2021), ‘Ethical and social risks of h...

work page internal anchor Pith review Pith/arXiv arXiv 2022