mlsanityData to 7 Oct 2026

Linear-LLM-SCM: Benchmarking LLMs for Coefficient Elicitation in Linear-Gaussian Causal Models

arXiv 2602.10282 ↗First posted 10 Feb 2026cs.LGcited by 1

On the map: LLM reasoning → Causal reasoning in LLMs

Abstract (arXiv)

Large language models (LLMs) have shown potential in identifying qualitative causal relations, but their ability to perform quantitative causal reasoning---estimating effect sizes that parametrize functional relationships---remains underexplored in continuous domains. We introduce Linear-LLM-SCM, a plug-and-play framework for evaluating LLMs on Linear Gaussian structural causal model parametrization when a directed acyclic graph (DAG) is given. The framework decomposes a DAG into local parent-child sets and prompts an LLM to produce a regression-style structural equation per node, which is aggregated and compared against available ground-truth parameters. Our experiments with seven real-world DAGs effect ground truth illustrate limitations of LLMs as quantitative causal parameterizers. Across most models, we observe variability in coefficient estimates and sensitivity to structural perturbations. We open-sourced the framework to further encourage the community to work on studies toward the use of LLM for causal effect elicitation in safety-critical domain, e.g., healthcare.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Causal reasoning in LLMs, one of 17 directions in LLM reasoning. Builds on 25 of its 49 references that are in the map. First posted on arXiv 10 Feb 2026.

Builds on

25 of 49 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Robust agents learn causal world models16 Feb
  2. 2023CLadder: Assessing Causal Reasoning in Language Models7 Dec
  3. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causality28 Apr

Other references · 22

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  3. 2018World Modelscited by 1,444
  4. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 820
  5. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 778
  6. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  7. 2023Causal Parrots: Large Language Models May Talk Causality But Are Not Causalcited by 146
  8. 2023Can large language models build causal graphs?cited by 72
  9. 2023Causal Discovery with Language Models as Imperfect Expertscited by 62
  10. 2024Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AIcited by 60
  11. 2023Understanding Causality with Large Language Models: Feasibility and Opportunitiescited by 53
  12. 2022DISCO: Distilling Counterfactuals with Large Language Modelscited by 29
10 more
  1. 2023Towards Causal Foundation Model: on Duality between Causal Inference and Attentioncited by 17
  2. 2025Extracting Probabilistic Knowledge from Large Language Models for Bayesian Network Parameterizationcited by 15
  3. 2023Data Augmentations for Improved (Large) Language Model Generalizationcited by 14
  4. 2024bnRep: A repository of Bayesian networks from the academic literaturecited by 9
  5. 2025Measuring Determinism in Large Language Models for Software Code Reviewcited by 6
  6. 2018Model-Preserving Sensitivity Analysis for Families of Gaussian Distributionscited by 4
  7. 2024Language Models as Causal Effect Generatorscited by 4
  8. 2020The FEDHC Bayesian network learning algorithmcited by 1
  9. 2025Your Assumed DAG is Wrong and Here's How To Deal With Itcited by 1
  10. 2025How Well Do LLMs Understand Drug Mechanisms? A Knowledge + Reasoning Evaluation Datasetcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026CausaLab: A Scalable Environment for Interactive Causal Discovery Toward AI Scientistscited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2024CausalGraph2LLM: Evaluating LLMs for Causal Queries
  2. 2025Ice Cream Doesn't Cause Drowning: Benchmarking LLMs Against Statistical Pitfalls in Causal Inference
  3. 2023Causal Structure Learning Supervised by Large Language Model
  4. 2024Causal Inference with Large Language Model: A Survey
  5. 2024Large Language Models and Causal Inference in Collaboration: A Survey
  6. 2025Structured Thinking Matters: Improving LLMs Generalization in Causal Inference Tasks
  7. 2026Can Post-Training Transform LLMs into Causal Reasoners?
  8. 2024CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs