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Source note3 min read

CausalMix: Data Mixture as Causal Inference for Language Model Training

Source

arXiv

arxiv.org/abs/2607.01104v1

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Plain-Language Brief

The paper presents CausalMix, a method that addresses the limitation of existing data mixing methods in Large Language Model (LLM) training by casting it as a causal inference problem. By using causal modeling to isolate confounding biases and dynamically infer state-dependent optimal data mixtures, CausalMix improves performance across multiple downstream tasks, outperforming other baselines.

What the Document Says

  • CausalMix is a method for optimizing data mixture in Large Language Model (LLM) training.
  • It casts the problem as a causal inference problem by treating statistical features of the data pool as covariates and domain mixture as treatment.
  • The authors use 512 runs of Qwen2.5-0.5B to estimate the Conditional Average Treatment Effect (CATE) and extrapolate the optimal mixture for an 800K data pool to train a 7B model.
  • They also generalize the framework to long chain-of-thought data on Qwen3-4B-Base.
  • By leveraging causal modeling, CausalMix aims to isolate confounding biases and dynamically infer state-dependent optimal data mixtures.
  • Experiments show that the mixture guided by CausalMix improves performance across multiple downstream tasks and outperforms RegMix and other baselines.
  • The authors also provide visual analysis of the learned mixing strategy using the CATE Interpreter.

Key Concepts Mentioned

  • Large Language Model (LLM)
  • Data Mixing
  • Causal Inference
  • Conditional Average Treatment Effect (CATE)
  • Chain-of-Thought Data

Claims Made by the Document

  • Existing data mixing methods have a limitation that they require costly retraining when the underlying data pool shifts.
  • CausalMix addresses this limitation by dynamically inferring state-dependent optimal data mixtures.
  • CausalMix outperforms RegMix and other baselines in extensive experiments.

Evidence Used

  • The authors describe that existing methods optimize mixture weights via proxy models but rely on the assumption of static data distributions, leading to the limitation mentioned.
  • They state that CausalMix improves performance across multiple downstream tasks and provide results to support this claim.
  • They also mention that they outperform RegMix and other baselines based on extensive experiments.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

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Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model performance. Recent methods optimize mixture weights via proxy models, but they rely on the assumption of static data distributions. As a result, when the underlying data pool shifts, these methods require costly retraining from scratch. This limitation restricts their ability to scale seamlessly from small settings to larger data pools and model sizes. In this paper, we propose CausalMix to address this limitation by casting data mixture optimization as a causal inference problem. We formulate the statistical features of the data pool as covariates and the domain mixture as the treatment. After fitting a causal model on 512 runs of Qwen2.5-0.5B to estimate the Conditional Average Treatment Effect (CATE), we extrapolate the optimal mixture for an 800K data pool and apply it to train a 7B model. Furthermore, we successfully generalize the framework to long chain-of-thought data on Qwen3-4B-Base. By leveraging causal modeling to isolate confounding biases, CausalMix dynamically infers state-dependent optimal data mixtures. Extensive experiments show that the mixture guided by CausalMix consistently improves performance across multiple downstream tasks, outperforming RegMix and other baselines. In addition, we use the CATE Interpreter to provide visual analysis of the learned mixing strategy. Overall, CausalMix offers a causal and interpretable framework for optimizing LLM data mixtures.