Table of Contents

• Relational Causal Models with Cycles: Representation and ReasoningRagib Ahsan, David Arbour, Elena Zheleva. 1-18 [doi]
• Towards efficient representation identification in supervised learningKartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas. 19-43 [doi]
• Weakly Supervised Discovery of Semantic AttributesAmeen Ali, Tomer Galanti, Evgenii Zheltonozhskii, Chaim Baskin, Lior Wolf. 44-69 [doi]
• VIM: Variational Independent Modules for Video PredictionRim Assouel, Lluís Castrejón, Aaron C. Courville, Nicolas Ballas, Yoshua Bengio. 70-89 [doi]
• Causal Explanations and XAISander Beckers. 90-109 [doi]
• Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundationsMichel Besserve, Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf. 110-143 [doi]
• Process Independence Testing in Proximal Graphical Event ModelsDebarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao, Dharmashankar Subramanian. 144-161 [doi]
• Typing assumptions improve identification in causal discoveryPhilippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle, Alexandre Drouin. 162-177 [doi]
• Disentangling Controlled Effects for Hierarchical Reinforcement LearningOriol Corcoll, Raul Vicente. 178-200 [doi]
• Interactive rank testing by bettingBoyan Duan, Aaditya Ramdas, Larry A. Wasserman. 201-235 [doi]
• Bivariate Causal Discovery via Conditional DivergenceBao Duong, Thin Nguyen. 236-252 [doi]
• Differentiable Causal Discovery Under Latent InterventionsGonçalo Rui Alves Faria, André F. T. Martins, Mário A. T. Figueiredo. 253-274 [doi]
• Selection, Ignorability and Challenges With Causal FairnessJake Fawkes, Robin Evans, Dino Sejdinovic. 275-289 [doi]
• Learning Invariant Representations with Missing DataMark Goldstein, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew C. Miller. 290-301 [doi]
• Info Intervention and its Causal CalculusHeyang Gong, Ke Zhu. 302-317 [doi]
• Partial Identification with Noisy Covariates: A Robust Optimization ApproachWenshuo Guo, Mingzhang Yin, Yixin Wang, Michael I. Jordan. 318-335 [doi]
• Simple data balancing achieves competitive worst-group-accuracyBadr Youbi Idrissi, Martín Arjovsky, Mohammad Pezeshki, David Lopez-Paz. 336-351 [doi]
• Predictive State Propensity Subclassification (PSPS): A causal inference algorithm for data-driven propensity score stratificationJoseph Kelly, Jing Kong, Georg M. Goerg. 352-372 [doi]
• Non-parametric Inference Adaptive to Intrinsic DimensionKhashayar Khosravi, Greg Lewis, Vasilis Syrgkanis. 373-389 [doi]
• Learning Causal Overhypotheses through Exploration in Children and Computational ModelsEliza Kosoy, Adrian Liu, Jasmine Collins, David M. Chan, Jessica B. Hamrick, Nan Rosemary Ke, Sandy H. Huang, Bryanna Kaufmann, John F. Canny, Alison Gopnik. 390-406 [doi]
• Causal Bandits without prior knowledge using separating setsArnoud A. W. M. de Kroon, Joris M. Mooij, Danielle Belgrave. 407-427 [doi]
• Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICASébastien Lachapelle, Pau Rodríguez, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien. 428-484 [doi]
• Data-driven exclusion criteria for instrumental variable studiesTony Liu 0004, Patrick N. Lawlor, Lyle Ungar, Konrad P. Kording. 485-508 [doi]
• Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series DataSindy Löwe, David Madras, Richard Z. Shilling, Max Welling. 509-525 [doi]
• Efficient Reinforcement Learning with Prior Causal KnowledgeYangyi Lu, Amirhossein Meisami, Ambuj Tewari. 526-541 [doi]
• A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous PopulationsAlex Markham, Richeek Das, Moritz Grosse-Wentrup. 542-558 [doi]
• CausalCity: Complex Simulations with Agency for Causal Discovery and ReasoningDaniel McDuff, Yale Song, Jiyoung Lee, Vibhav Vineet, Sai Vemprala, Nicholas Gyde, Hadi Salman, Shuang Ma, Kwanghoon Sohn, Ashish Kapoor. 559-575 [doi]
• Equality Constraints in Linear Hawkes ProcessesSøren Wengel Mogensen. 576-593 [doi]
• Optimal Training of Fair Predictive ModelsRazieh Nabi, Daniel Malinsky, Ilya Shpitser. 594-617 [doi]
• Differentially Private Estimation of Heterogeneous Causal EffectsFengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana 0001, Donald Ngwe, Aadharsh Kannan. 618-633 [doi]
• On the Equivalence of Causal Models: A Category-Theoretic ApproachJun Otsuka, Hayato Saigo. 634-646 [doi]
• Diffusion Causal Models for Counterfactual EstimationPedro Sanchez, Sotirios A. Tsaftaris. 647-668 [doi]
• Causal Structure Discovery between Clusters of Nodes Induced by Latent FactorsChandler Squires, Annie Yun, Eshaan Nichani, Raj Agrawal, Caroline Uhler. 669-687 [doi]
• Causal Imputation via Synthetic InterventionsChandler Squires, Dennis Shen, Anish Agarwal, Devavrat Shah, Caroline Uhler. 688-711 [doi]
• Estimating Social Influence from Observational DataDhanya Sridhar, Caterina De Bacco, David M. Blei. 712-733 [doi]
• Identifying Principal Stratum Causal Effects Conditional on a Post-treatment Intermediate ResponseXiaoqing Tan, Judah Abberbock, Priya Rastogi, Gong Tang. 734-753 [doi]
• Attainability and Optimality: The Equalized Odds Fairness RevisitedZeyu Tang, Kun Zhang. 754-786 [doi]
• Same Cause; Different Effects in the BrainMariya Toneva, Jennifer Williams, Anand Bollu, Christoph Dann, Leila Wehbe. 787-825 [doi]
• A Multivariate Causal Discovery based on Post-Nonlinear ModelKento Uemura, Takuya Takagi, Kambayashi Takayuki, Hiroyuki Yoshida, Shohei Shimizu. 826-839 [doi]
• Local Constraint-Based Causal Discovery under Selection BiasPhilip Versteeg, Joris M. Mooij, Cheng Zhang. 840-860 [doi]
• A Uniformly Consistent Estimator of non-Gaussian Causal Effects Under the k-Triangle-Faithfulness AssumptionShuyan Wang, Peter Spirtes. 861-876 [doi]
• Identifying Coarse-grained Independent Causal Mechanisms with Self-supervisionXiaoyang Wang, Klara Nahrstedt, Oluwasanmi Koyejo. 877-903 [doi]
• Integrative R-learner of heterogeneous treatment effects combining experimental and observational studiesLili Wu, Shu Yang. 904-926 [doi]
• Fair Classification with Instance-dependent Label NoiseSonghua Wu, Mingming Gong, Bo Han 0003, Yang Liu 0018, Tongliang Liu. 927-943 [doi]
• Causal Discovery in Linear Structural Causal Models with Deterministic RelationsYuqin Yang, Mohamed S. Nafea, AmirEmad Ghassami, Negar Kiyavash. 944-993 [doi]
• Causal Discovery for Linear Mixed DataYan Zeng 0002, Shohei Shimizu, Hidetoshi Matsui, Fuchun Sun. 994-1009 [doi]
• Can Humans Be out of the Loop?Junzhe Zhang, Elias Bareinboim. 1010-1025 [doi]
• Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized TextsBo Zhang, Jiayao Zhang 0001. 1026-1036 [doi]