We present a hybrid constraint-based/Bayesian algorithm for learning causal networks in the presence of sparse data. The algorithm searches the space of equivalence classes of models (essential graphs) using a heuristic based on conventional constraint-based techniques. Each essential graph is then converted into a directed acyclic graph and scored using a Bayesian scoring metric. Two variants of the algorithm are developed and tested using data from randomly generated networks of sizes from 15 to 45 nodes with data sizes ranging from 250 to 2000 records. Both variations are compared to, and found to consistently outperform two variations of greedy search with restarts.
Scaling up Greedy Causal Search for Continuous Variables
Center for Causal Discovery（因果发现中心）
作为R或Tetrad程序中的标准实现，科学家最广泛或最有效地使用的因果搜索算法具有严重的维度限制，使其不适合大数据问题，而不会牺牲准确性。然而，执行方面的改进是可能的。我们探索贪婪等价搜索的优化，允许在13分钟内搜索50，000个变量的问题，在四个处理器，16 G膝上计算机上的1000个样本的稀疏模型。我们在匹兹堡超级计算中心的超级计算机节点上用18小时完成了1000个样本对1，000，000个变量的稀疏模型的问题，该节点具有40个处理器和384 G RAM。同样的算法可以应用于离散数据，但离散得分较慢，尽管离散实现目前在我们的实验中没有扩展;我们已经设法在具有1000个样本的稀疏模型中扩展到约10，000个变量。
Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?
Department of Mathematics, ETH Zurich（苏黎世联邦理工学院数学系）；Department of Statistics, UC Berkeley（伯克利大学统计系）；German Center for Neurodegenerative Diseases (DZNE) & University of Bonn（德国神经退行性疾病研究中心（DZNE）及波恩大学）；MRC Biostatistics Unit, University of Cambridge（剑桥大学医学研究委员会生物统计学单位）
Causal Language Control in Multilingual Transformers via Sparse Feature Steering
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Maryland, College Park（马里兰大学学院公园分校）；Barnard College（巴纳德学院）；Algoverse AI Research（Algoverse人工智能研究）；Meta FAIR Lab（Meta FAIR实验室）
CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院通用人工智能国家重点实验室）；National Institute of Health Data Science, and Institute for Artificial Intelligence, Peking University（北京大学健康医疗大数据国家研究院、人工智能研究院）
