Detecting and measuring confounding effects from data is a key challenge in causal inference. Existing methods frequently assume causal sufficiency, disregarding the presence of unobserved confounding variables. Causal sufficiency is both unrealistic and empirically untestable. Additionally, existing methods make strong parametric assumptions about the underlying causal generative process to guarantee the identifiability of confounding variables. Relaxing the causal sufficiency and parametric assumptions and leveraging recent advancements in causal discovery and confounding analysis with non-i.i.d. data, we propose a comprehensive approach for detecting and measuring confounding. We consider various definitions of confounding and introduce tailored methodologies to achieve three objectives: (i) detecting and measuring confounding among a set of variables, (ii) separating observed and unobserved confounding effects, and (iii) understanding the relative strengths of confounding bias between different sets of variables. We present useful properties of a confounding measure and present measures that satisfy those properties. Empirical results support the theoretical analysis.
How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding
Department of Information and Computing Sciences, Utrecht University（乌得勒支大学信息与计算科学系）；Leiden Institute of Advanced Computer Science, Leiden University（莱顿大学莱顿高级计算机科学研究所）
Estimation of linear, non-gaussian causal models in the presence of confounding latent variables
从数据中估计线性因果模型（也称为结构方程模型）是一个众所周知的问题，在过去受到了很多关注。然而，大多数以前的工作，提出了明确或隐含的高斯假设，限制了模型的可识别性。我们最近证明了（Shimizu et al，2005; Hoyer et al，2006），对于非高斯分布，完全因果模型可以在无隐变量的情况下估计。在这篇文章中，我们讨论了模型的估计时，混淆潜变量存在。虽然在这种情况下不再保证唯一性，但最多有一组有限的模型可以拟合数据。我们开发了一个算法来估计这一套，并描述了数值模拟，证实了理论的论点，并证明了该方法的实际可行性。完整的Matlab代码提供了所有的模拟。
Causal Flow Q-Learning for Robust Offline Reinforcement Learning
Causal AI Lab, Columbia University, NY, United States（因果AI实验室，哥伦比亚大学，纽约，美国）；Department of Electrical Engineering and Computer Science, Syracuse University, NY, United States（电气工程与计算机科学系，雪城大学，纽约，美国）
Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference
School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；International Campus, Zhejiang University（浙江大学国际校区）；WeChat Search Application Department, Tencent（腾讯微信搜索应用部）；Pazhou Lab（琶洲实验室）
