Causal Effect Estimation with Latent Textual Treatments
School of Computer Science and Engineering, The Hebrew University of Jerusalem（计算机科学与工程学院，特拉维夫大学）；Department of Economics, Stanford University（斯坦福大学经济系）；Graduate School of Business, Stanford University（斯坦福大学商学院）
Omri Feldman, Amar Venugopal, Jann Spiess, Amir Feder
Understanding the causal effects of text on downstream outcomes is a central task in many applications. Estimating such effects requires researchers to run controlled experiments that systematically vary textual features. While large language models (LLMs) hold promise for generating text, producing and evaluating controlled variation requires more careful attention. In this paper, we present an end-to-end pipeline for the generation and causal estimation of latent textual interventions. Our work first performs hypothesis generation and steering via sparse autoencoders (SAEs), followed by robust causal estimation. Our pipeline addresses both computational and statistical challenges in text-as-treatment experiments. We demonstrate that naive estimation of causal effects suffers from significant bias as text inherently conflates treatment and covariate information. We describe the estimation bias induced in this setting and propose a solution based on covariate residualization. Our empirical results show that our pipeline effectively induces variation in target features and mitigates estimation error, providing a robust foundation for causal effect estimation in text-as-treatment settings.
Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning
Tencent BAC（腾讯BAC）；Shenzhen International Graduate School, Tsinghua University（深圳国际研究生院，清华大学）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；School of Mathematics and Statistics, University of Glasgow（格拉斯哥大学数学与统计学学院）
University of Oxford, United Kingdom（牛津大学）；Queen Mary University of London, United Kingdom（伦敦玛丽女王大学）；Peking University, China（北京大学）；University of Bristol, United Kingdom（布里斯托大学）；King's College London, United Kingdom（伦敦国王学院）；Hong Kong University of Science（香港科学大学）；University College London, United Kingdom（伦敦大学学院）
Perception Without Engagement: Dissecting the Causal Discovery Deficit in LMMs
Harbin Institute of Technology（哈尔滨工业大学）；Pengcheng Laboratory（鹏城实验室）；National University of Singapore（新加坡国立大学）；Peking University（北京大学）；Harvard University（哈佛大学）
