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arxiv: 2605.09079 · v1 · submitted 2026-05-09 · 💻 cs.AI

Recognition: 1 theorem link

· Lean Theorem

CauSim: Scaling Causal Reasoning with Increasingly Complex Causal Simulators

Nicol\'as Astorga , Anita Kriz , Mihaela van der Schaar
Authors on Pith no claims yet

Pith reviewed 2026-05-12 02:29 UTC · model grok-4.3

classification 💻 cs.AI
keywords causal reasoningstructural causal modelslarge language modelscausal simulatorsdata augmentationcurriculum scalingself-improvementcausal queries
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The pith

CauSim lets LLMs incrementally build executable causal simulators that scale while keeping query answers verifiable.

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

The paper introduces CauSim to solve the scarcity of labeled data for training large language models on causal reasoning. It does so by having the models themselves construct executable structural causal models step by step, growing their complexity while preserving the ability to verify answers to causal questions. This process converts between non-executable causal descriptions and code, which supports data augmentation from existing knowledge and supervision in representations that were previously difficult to use. Experiments demonstrate that performance improves with greater simulator complexity and data volume, that models can improve themselves using the data they generate, and that the approach generalizes across different forms of causal knowledge.

Core claim

CauSim constructs increasingly complex causal simulators as executable structural causal models built incrementally by LLMs. These simulators reach globally complex systems while maintaining verifiable ground-truth answers to causal queries. The framework formalizes non-executable causal knowledge into code for augmentation and translates the simulators back into natural language to enable supervision where it was previously scarce.

What carries the argument

CauSim, a framework that has LLMs incrementally construct executable structural causal models to produce scalable simulators with verifiable causal query answers.

If this is right

  • Training LLMs on data generated from these simulators produces consistent gains in causal reasoning performance.
  • Performance continues to improve as simulator complexity increases through curriculum ordering and as data volume grows.
  • LLMs achieve self-improvement on causal tasks by using simulators they have generated themselves.
  • Non-executable domain knowledge can be formalized into executable simulators to augment training data.

Where Pith is reading between the lines

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

  • The same incremental construction process could be tested as a way to bootstrap causal models from observational datasets in specific application areas.
  • Generated simulators could serve as controlled environments for measuring the limits of current causal reasoning techniques before applying them to real systems.
  • Detecting and correcting construction errors during the incremental build would allow the method to reach even larger scales.

Load-bearing premise

Large language models can incrementally construct these complex simulators without introducing errors that invalidate the ground-truth causal relationships or the correctness of query answers.

What would settle it

Direct verification that answers to causal queries computed from the generated simulator match the answers derived from its underlying causal graph structure, particularly as the number of variables and relations grows.

Figures

Figures reproduced from arXiv: 2605.09079 by Anita Kriz, Mihaela van der Schaar, Nicol\'as Astorga.

Figure 1
Figure 1. Figure 1: Overview of CauSim. (Left) Causal reasoning faces three challenges: [C1] scale, [C2] non-executable representations, and [C3] lack of ground-truth supervision. (Right) CauSim addresses these through increasingly complex causal simulators: SCMs are built incrementally to scale complexity [C1]; non-executable causal knowledge is formalized into executable code and executable SCMs are informalized into natura… view at source ↗
Figure 2
Figure 2. Figure 2: Node-by-node generation im￾proves SCM generation at scale. Method. We compare two methods for constructing an SCM with n structural mechanisms and n corresponding exogenous samplers as executable code: (i) One-shot generation: the LLM is prompted to output the full SCM in a single specification step and (ii) Incremental generation: the LLM first specifies a 1-node SCM, and then incrementally grows by addin… view at source ↗
Figure 3
Figure 3. Figure 3: Improving Causal Reasoning Across Distributions General Setup. Unless otherwise noted, the following setup is held constant across experiments. Data. We use our incremental SCM generation process (Sec. 4) to create a training set of Python￾executable SCMs over 4-letter meaningless variables, which we term the NONSENSE-code dataset. We use GPT-5 to generate SCMs and enforce three topologies: inverted stars,… view at source ↗
Figure 4
Figure 4. Figure 4: Curriculum Matters. 4 5 6 7 8 9 Complexity levels 0.0 0.2 0.4 pass@1 intervention 4 5 6 7 8 9 Complexity levels 0.0 0.2 0.4 counterfactual base 5 15 45 135 [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Self-Improvement Motivation. A natural question is whether a model can use the framework to bootstrap its own causal reasoning ability by training on its own generated data. A related question is how much performance depends on the quality of the generator used to produce SCMs, and whether self-generated SCMs are as effective as those produced by stronger models. Method. We study self-improvement by varyin… view at source ↗
Figure 7
Figure 7. Figure 7: Data Augmentation Motivation. In many real-world settings, data is generated by an underlying, unobserved SCM. When such domain knowl￾edge can be formalized, even approximately, it can simulate interventional and counterfactual data, providing a principled way to expand causal supervision beyond the original dataset. Method. We construct a controlled “original” dataset derived from the NIH Stroke Scale (NI… view at source ↗
Figure 8
Figure 8. Figure 8: Training on formal causal queries. Pass@1 accuracy by causal query type for models trained with and without simulator-generated causal queries. Fine-tuning with causal-simulator data consistently improves performance over the base model. M.2 Evaluation of Causal-Simulator Data Augmentation We evaluate the value of transforming an existing informal SCM into executable form, automatically generating addition… view at source ↗
Figure 9
Figure 9. Figure 9: Joint results across all causal query types. Results for deduction, abduction, intervention, and counterfactual queries. 49 [PITH_FULL_IMAGE:figures/full_fig_p049_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Ablation varying the amount of training data. Test results on NONSENSE-formal SCMs for intervention and counterfactual queries, averaged across structures. Models are trained jointly on counterfactual and intervention causal queries. We show levels 1-10. 50 [PITH_FULL_IMAGE:figures/full_fig_p050_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Ablation varying the amount of training data. Test results on NONSENSE-formal SCMs for intervention and counterfactual queries, averaged across structures. Models are trained jointly on counterfactual and intervention causal queries. We show levels 4-9. 51 [PITH_FULL_IMAGE:figures/full_fig_p051_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Ablation varying the curriculum. Test results on NONSENSE-formal SCMs for intervention and counterfactual queries, averaged across structures. Models are trained jointly on counterfactual and intervention causal queries. We show levels 1-10. 52 [PITH_FULL_IMAGE:figures/full_fig_p052_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Ablation varying the curriculum. Test results on NONSENSE-formal SCMs for intervention and counterfactual queries, averaged across structures. Models are trained jointly on counterfactual and intervention causal queries. We show levels 4-9. 53 [PITH_FULL_IMAGE:figures/full_fig_p053_13.png] view at source ↗
read the original abstract

Despite surpassing human performance across mathematics, coding, and other knowledge-intensive tasks, large language models (LLMs) continue to struggle with causal reasoning. A core obstacle is the target data itself: causal systems are complex and often expressed in non-executable forms, while ground-truth answers to causal queries are inherently scarce. We introduce CauSim, a framework that turns causal reasoning from a scarce-label problem into a scalable supervised one. CauSim constructs increasingly complex causal simulators: executable structural causal models (SCMs), incrementally built by LLMs, that scale to globally complex systems while maintaining verifiable answers to causal queries. CauSim operates across representations by formalizing non-executable causal knowledge into code, enabling data augmentation, and translating executable SCMs into natural language, enabling supervision in previously difficult-to-supervise representations. We structure our research into two parts: (1) how to construct increasingly complex causal simulators, and (2) a systematic study of what CauSim enables, demonstrating generalization across representations, consistent gains from curriculum scaling and data volume, LLM self-improvement through self-generated simulators, and data augmentation via formalization of existing domain knowledge.

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 / 1 minor

Summary. The paper introduces CauSim, a framework that uses LLMs to incrementally construct executable structural causal models (SCMs) as simulators for causal reasoning. These simulators scale in complexity while providing verifiable ground-truth answers to causal queries. The approach formalizes non-executable causal knowledge into code for data augmentation and translates SCMs back to natural language for supervision. Research is structured in two parts: (1) methods for building increasingly complex causal simulators and (2) empirical studies demonstrating generalization across representations, gains from curriculum scaling and data volume, LLM self-improvement via self-generated simulators, and augmentation from domain knowledge.

Significance. If the central claims hold, CauSim would address the scarcity of causal data and supervision signals for LLMs by converting causal reasoning into a scalable supervised learning problem with independent executable ground truth. The curriculum scaling, cross-representation generalization, and self-improvement results could represent a meaningful advance. The use of executable SCMs is a strength for verifiability, though the self-improvement component carries a noted risk of circularity that requires careful validation.

major comments (2)
  1. The abstract and Part (1) description of incremental SCM construction by LLMs claims scaling to globally complex systems while maintaining verifiable causal query answers, but without detailed algorithms for error detection, propagation analysis, or empirical fidelity metrics in the provided text, it is unclear whether construction errors invalidate ground-truth relationships. This is load-bearing for the core claim of reliable simulators.
  2. In the self-improvement study (Part 2), the loop of LLMs generating and training on their own simulators risks circularity if verification of causal correctness ultimately depends on the same model capabilities rather than fully independent executable checks. Concrete ablation results separating model-generated data from external validation would be needed to support the self-improvement claim.
minor comments (1)
  1. The abstract would benefit from explicit definitions or metrics for 'increasingly complex' and 'globally complex systems' to make the scaling claims more precise.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful review and constructive feedback on our manuscript. We address each major comment point by point below, providing clarifications on the mechanisms that support reliable simulator construction and independent verification.

read point-by-point responses

  1. Referee: The abstract and Part (1) description of incremental SCM construction by LLMs claims scaling to globally complex systems while maintaining verifiable causal query answers, but without detailed algorithms for error detection, propagation analysis, or empirical fidelity metrics in the provided text, it is unclear whether construction errors invalidate ground-truth relationships. This is load-bearing for the core claim of reliable simulators.

    Authors: We agree that the manuscript would benefit from greater explicitness on these aspects. The incremental construction procedure incorporates execution-based consistency checks after each addition of a variable or edge: a battery of causal queries is run on the updated executable SCM and compared against results from the prior verified state. Inconsistencies arising from construction errors are rejected before the simulator is accepted. While the current text describes this process at a high level, we will add a dedicated subsection with pseudocode for the error-detection routine, a formal propagation analysis, and quantitative fidelity metrics (e.g., query-consistency rates across build steps) in the revised version. revision: yes

  2. Referee: In the self-improvement study (Part 2), the loop of LLMs generating and training on their own simulators risks circularity if verification of causal correctness ultimately depends on the same model capabilities rather than fully independent executable checks. Concrete ablation results separating model-generated data from external validation would be needed to support the self-improvement claim.

    Authors: Verification of each generated simulator occurs exclusively through direct execution of its code, which supplies ground-truth answers independently of any LLM reasoning. The self-improvement experiments already include controls that train on simulators built from external domain knowledge and compare against purely self-generated ones; performance gains remain when evaluation is restricted to execution-derived labels. To further address the circularity concern, we will expand the results with additional ablations that explicitly isolate model-generated data from any model-assisted validation steps. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The provided manuscript text consists only of the abstract and a placeholder for the full paper, with no equations, self-citations, or derivation steps that reduce any claim to its own inputs by construction. The framework's core elements—LLM-driven construction of executable SCMs, formalization across representations, and curriculum scaling—are presented as independent mechanisms that generate verifiable ground truth via code execution rather than through self-referential fitting or imported uniqueness theorems. No load-bearing step matches the enumerated circularity patterns, and the self-improvement aspect is described as enabled by the external executability of the simulators, keeping the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim depends on the domain assumption that structural causal models can be made executable to yield verifiable answers, and that LLMs can build them incrementally without breaking this property. No free parameters or invented physical entities are described.

axioms (1)
  • domain assumption Structural causal models can be represented as executable programs that provide verifiable ground-truth answers to causal queries
    This assumption enables the conversion of causal knowledge into scalable supervised data.
invented entities (1)
  • CauSim framework no independent evidence
    purpose: To construct and scale complex causal simulators for training
    Newly introduced method; no independent evidence provided beyond the framework description itself.

pith-pipeline@v0.9.0 · 5504 in / 1302 out tokens · 48227 ms · 2026-05-12T02:29:23.746767+00:00 · methodology

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