arXiv cs.AI· Ziyi Ding, Xiaodi Huang, Xianxin Lai, Haijier Chen, Zhang Zhang, Xiao-Ping Zhang, Jiayu Chen·· 12 小时前AI 评分31
Intervene, Calibrate, Discover：从像素中学习因果结构
Intervene, Calibrate, Discover: Learning Causal Structure from Pixels
AI 导读
研究提出“干预—校准—发现”框架，用固定轮转干预获得的验证值校准图像读数，解决视觉读出误差导致的模型设置与环境干预值错位问题。在编码器与解码器冻结的前提下，维度可逆校准映射将视觉响应与目标设置统一到同一数值单位，随后智能体选择干预以确定图方向并拟合局部机制。
正文
Abstract:Learning high-level causal variables from low-level observations is a central problem for artificial intelligence. With known node semantics but an unknown graph, visual readout errors can misalign model settings with environment intervention values. We address this mismatch by pairing image readouts with verified values attained through fixed round-robin interventions. With encoder and decoder frozen, a dimension-wise invertible calibration map puts visual responses and target settings in shared numerical units. After freezing this map, the agent selects interventions to resolve graph directions and fits local mechanisms to predict consequences; inverse calibration returns counterfactual states to the decoder's original coordinates. Across Domino, Pick-and-Place, Balance, Gears, CAUSAL3D, and CausalWorld, we evaluate graph discovery, intervention consequences, and counterfactual images. Controlled domino experiments show that interventions resolve orientation ambiguity left by equal additional observations. In a matched-data domino control, calibration reduces the mean error of predicted changes in other variables within the same factual context, relative to an uncalibrated model.
| Subjects: | Artificial Intelligence (cs.AI); Applications (stat.AP) |
| Cite as: | arXiv:2610.34711 [cs.AI] |
| (or arXiv:2610.34711v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34711 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ziyi Ding [view email]
[v1]
Mon, 26 Oct 2026 02:01:46 UTC (723 KB)
来源：arXiv cs.AI · arxiv.org