FoundCause: Causal Discovery with Latent Confounders from Observational Data
FoundCause：含潜在混杂的观测数据因果发现AI 解读 ↓
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FoundCause：含潜在混杂的观测数据因果发现
- 研究问题
- 在没有干预数据且存在潜在共同原因时，如何从观测数据同时恢复因果方向与隐藏混杂，并提高跨数据集推断效率？
- 方法与思路
- 作者以合成结构因果模型训练摊销式模型，结合置换不变注意力、成对统计特征、图结构细化及潜在混杂模块。
- 材料陈述的贡献
- 摘要称，该模型通过单次前向传播生成因果图，并在十五个真实数据集上相对所比较方法取得更好的图恢复指标。
- 阅读关注点
- 应核对潜在混杂的图表示、训练模拟分布与测试数据的差异，以及真实因果图来源、比较设置和缺失数据评估。
- 阅读边界
- 这是预印本，同行评审与期刊接收均未核实；性能和首创性均为作者陈述，摘要未说明可识别条件或充分评估细节。
原文摘要
Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions. We propose FoundCause, an amortized causal discovery model trained entirely on synthetic data that maps datasets directly to causal graphs in a single forward pass. By learning from large collections of simulated structural causal models, FoundCause captures transferable statistical patterns that generalize beyond individual datasets. The architecture incorporates several key inductive biases for causal discovery. It uses a permutation-invariant transformer encoder with alternating attention over samples and variables to jointly model cross-variable dependence and per-variable distributions. Pairwise statistical features derived from classical asymmetry measures are injected through statistics-conditioned attention, guiding the model toward known causal signals. A factorized decoder separates edge existence from direction, while a triangular refinement module enables reasoning over higher-order causal motifs such as chains and colliders. In addition, a dedicated confounder module based on learnable latent tokens explicitly models hidden common causes, and the model explicitly handles missing data via its masked input representation. To our knowledge, FoundCause is the first amortized causal discovery approach to explicitly model latent confounding. FoundCause outperforms 11 classical non-amortized methods (e.g., PC, GES, NOTEARS-style optimization) and 4 amortized causal discovery methods on 15 real-world datasets, achieving +9.6% improvement in $F_1$, +1.2% in AUROC, and an 18.9% reduction in structural Hamming distance relative to the strongest non-amortized methods, while performing inference in a single forward pass.