Causal structure learning (CSL) refers to the task of learning causal relationships from data. Advances in CSL now allow learning of causal graphs in diverse application domains, which has the potential to facilitate data-driven causal decision-making. Real-world CSL performance depends on a number of $\textit{context-specific}$ factors, including context-specific data distributions and non-linear dependencies, that are important in practical use-cases. However, our understanding of how to assess and select CSL methods in specific contexts remains limited. To address this gap, we present $\textit{CausalRegNet}$, a multiplicative effect structural causal model that allows for generating observational and interventional data incorporating context-specific properties, with a focus on the setting of gene perturbation experiments. Using real-world gene perturbation data, we show that CausalRegNet generates accurate distributions and scales far better than current simulation frameworks. We illustrate the use of CausalRegNet in assessing CSL methods in the context of interventional experiments in biology.
$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery
Technical University of Munich（慕尼黑工业大学）；Robert Bosch GmbH（罗伯特·博世有限公司）；University of Applied Sciences Kempten（肯普滕应用科学大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Technical University of Munich（慕尼黑工业大学）；Mila - Quebec AI Institute（米拉 - 魁北克人工智能研究所）；ServiceNow Research（ServiceNow研究院）；Université de Montréal（蒙特利尔大学）；Helmholtz AI（亥姆霍兹人工智能研究所）
Linear-LLM-SCM: Benchmarking LLMs for Coefficient Elicitation in Linear-Gaussian Causal Models
Data Science and its Applications, German Research Centre for Artificial Intelligence (DFKI)（德国人工智能研究中心数据科学与应用部门）；Dept. of Computer Science, University of Kaiserslautern–Landau (RPTU)（科隆-兰道大学计算机科学系）；Digital Health - Machine Learning Research Group, Hasso Plattner Institute for Digital Engineering（哈索·普朗纳研究所数字工程学院数字健康-机器学习研究组）；Institute of Informatics, University of Munich (LMU)（慕尼黑大学信息学院）；Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai（西奈山医学院哈索·普朗纳研究所数字健康中心）；Munich Center for Machine Learning (MCML), Germany（慕尼黑机器学习中心）
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Mila（米拉研究所）；Polytechnique Montréal（蒙特利尔综合理工学院）；Deepmind（深度思维）；Element AI（Element AI公司）；Google AI（谷歌人工智能）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
CNRS, MAP5（法国国家科学研究中心，MAP5）；Université Paris Cité（巴黎西岱大学）；CentraleSupélec（中央理工-高等电力学院）；Université Paris-Saclay（巴黎-萨克雷大学）；University of Copenhagen（哥本哈根大学）
Targeted Smooth Bayesian Causal Forests: An analysis of heterogeneous treatment effects for simultaneous versus interval medical abortion regimens over gestation
Causal Inference with Video Features as Treatments
John F. Kennedy School of Government, Harvard University（哈佛大学肯尼迪政府学院）；Dartmouth College（达特茅斯学院）；University of Oklahoma（俄克拉荷马大学）；Purdue University（普渡大学）；Harvard University（哈佛大学）
A Neuroimaging Simulation Framework for Developing and Evaluating Causal AI
Biomedical Engineering Graduate Program, University of Calgary（卡莱尔大学生物医学工程研究生项目）；Department of Computing, Imperial College London（伦敦帝国理工学院计算机系）；Department of Radiology, University of Michigan（密歇根大学放射科）
CausalGame: Benchmarking Causal Thinking of LLM Agents in Games
MBZUAI（穆罕默德·本·扎耶德人工智能大学）；Carnegie Mellon University（卡内基梅隆大学）；Hong Kong Baptist University（香港浸会大学）；University of Oxford（牛津大学）；New York University, Abu Dhabi（纽约大学阿布扎比分校）
Can Language Models Boost the Power of Randomized Experiments Without Statistical Bias?
University of California, Berkeley（加州大学伯克利分校）；University of California, San Diego（加州大学圣迭戈分校）；Michael G. Foster School of Business, University of Washington（华盛顿大学迈克尔G福斯特商学院）；University of Southern California（南加州大学）
