Causal thinking enables humans to understand not just what is seen, but why it happens. To replicate this capability in modern AI systems, we introduce the task of visual causal discovery. It requires models to infer cause-and-effect relations among visual entities across diverse scenarios instead of merely perceiving their presence. To this end, we first construct the Visual Causal Graph dataset (VCG-32K), a large-scale collection of over 32,000 images annotated with entity-level causal graphs, and further develop CauSight, a novel vision-language model to perform visual causal discovery through causally aware reasoning. Our training recipe integrates three components: (1) training data curation from VCG-32K, (2) Tree-of-Causal-Thought (ToCT) for synthesizing reasoning trajectories, and (3) reinforcement learning with a designed causal reward to refine the reasoning policy. Experiments show that CauSight outperforms GPT-4.1 on visual causal discovery, achieving over a threefold performance boost (21% absolute gain). Our code, model, and dataset are fully open-sourced at project page: https://github.com/OpenCausaLab/CauSight.

翻译：因果思维使人类不仅能理解所见之物，更能理解其发生的原因。为在现代人工智能系统中复现这一能力，我们提出了视觉因果发现任务。该任务要求模型在多样化场景中推断视觉实体间的因果关系，而非仅感知其存在。为此，我们首先构建了视觉因果图数据集（VCG-32K），这是一个包含超过32,000张图像的大规模数据集，每张图像均标注了实体级因果图；并进一步开发了CauSight，一种新颖的视觉语言模型，通过因果感知推理执行视觉因果发现。我们的训练方案整合了三个组成部分：（1）基于VCG-32K的训练数据整理，（2）用于合成推理轨迹的因果思维树（ToCT），以及（3）结合设计的因果奖励的强化学习，以优化推理策略。实验表明，CauSight在视觉因果发现任务上优于GPT-4.1，实现了超过三倍的性能提升（绝对增益21%）。我们的代码、模型和数据集已在项目页面完全开源：https://github.com/OpenCausaLab/CauSight。