CLeaR 2022

48 papers

A Distance Covariance-Based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations Alex Markham, Richeek Das, Moritz Grosse-Wentrup

A Multivariate Causal Discovery Based on Post-Nonlinear Model Kento Uemura, Takuya Takagi, Kambayashi Takayuki, Hiroyuki Yoshida, Shohei Shimizu

A Uniformly Consistent Estimator of Non-Gaussian Causal Effects Under the $k$-Triangle-Faithfulness Assumption Shuyan Wang, Peter Spirtes

Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data Sindy Löwe, David Madras, Richard Zemel, Max Welling

Attainability and Optimality: The Equalized Odds Fairness Revisited Zeyu Tang, Kun Zhang

Bivariate Causal Discovery via Conditional Divergence Bao Duong, Thin Nguyen

Can Humans Be Out of the Loop? Junzhe Zhang, Elias Bareinboim

Causal Bandits Without Prior Knowledge Using Separating Sets Arnoud De Kroon, Joris Mooij, Danielle Belgrave

Causal Discovery for Linear Mixed Data Yan Zeng, Shohei Shimizu, Hidetoshi Matsui, Fuchun Sun

Causal Discovery in Linear Structural Causal Models with Deterministic Relations Yuqin Yang, Mohamed S Nafea, AmirEmad Ghassami, Negar Kiyavash

Causal Explanations and XAI Sander Beckers

Causal Imputation via Synthetic Interventions Chandler Squires, Dennis Shen, Anish Agarwal, Devavrat Shah, Caroline Uhler

Causal Structure Discovery Between Clusters of Nodes Induced by Latent Factors Chandler Squires, Annie Yun, Eshaan Nichani, Raj Agrawal, Caroline Uhler

CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning Daniel McDuff, Yale Song, Jiyoung Lee, Vibhav Vineet, Sai Vemprala, Nicholas Alexander Gyde, Hadi Salman, Shuang Ma, Kwanghoon Sohn, Ashish Kapoor

Cause-Effect Inference Through Spectral Independence in Linear Dynamical Systems: Theoretical Foundations Michel Besserve, Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf

Data-Driven Exclusion Criteria for Instrumental Variable Studies Tony Liu, Patrick Lawlor, Lyle Ungar, Konrad Kording

Differentiable Causal Discovery Under Latent Interventions Gonçalo Rui Alves Faria, Andre Martins, Mario A. T. Figueiredo

Differentially Private Estimation of Heterogeneous Causal Effects Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan

Diffusion Causal Models for Counterfactual Estimation Pedro Sanchez, Sotirios A. Tsaftaris

Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA Sebastien Lachapelle, Pau Rodriguez, Yash Sharma, Katie E Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien

Disentangling Controlled Effects for Hierarchical Reinforcement Learning Oriol Corcoll, Raul Vicente

Efficient Reinforcement Learning with Prior Causal Knowledge Yangyi Lu, Amirhossein Meisami, Ambuj Tewari

Equality Constraints in Linear Hawkes Processes Søren Wengel Mogensen

Estimating Social Influence from Observational Data Dhanya Sridhar, Caterina De Bacco, David Blei

Fair Classification with Instance-Dependent Label Noise Songhua Wu, Mingming Gong, Bo Han, Yang Liu, Tongliang Liu

Identifying Coarse-Grained Independent Causal Mechanisms with Self-Supervision Xiaoyang Wang, Klara Nahrstedt, Oluwasanmi O Koyejo

Identifying Principal Stratum Causal Effects Conditional on a Post-Treatment Intermediate Response Xiaoqing Tan, Judah Abberbock, Priya Rastogi, Gong Tang

Info Intervention and Its Causal Calculus Heyang Gong, Ke Zhu

Integrative $r$-Learner of Heterogeneous Treatment Effects Combining Experimental and Observational Studies Lili Wu, Shu Yang

Interactive Rank Testing by Betting Boyan Duan, Aaditya Ramdas, Larry Wasserman

Learning Causal Overhypotheses Through Exploration in Children and Computational Models Eliza Kosoy, Adrian Liu, Jasmine L Collins, David Chan, Jessica B Hamrick, Nan Rosemary Ke, Sandy Huang, Bryanna Kaufmann, John Canny, Alison Gopnik

Learning Invariant Representations with Missing Data Mark Goldstein, Joern-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew Miller

Local Constraint-Based Causal Discovery Under Selection Bias Philip Versteeg, Joris Mooij, Cheng Zhang

Non-Parametric Inference Adaptive to Intrinsic Dimension Khashayar Khosravi, Greg Lewis, Vasilis Syrgkanis

On the Equivalence of Causal Models: A Category-Theoretic Approach Jun Otsuka, Hayato Saigo

Optimal Training of Fair Predictive Models Razieh Nabi, Daniel Malinsky, Ilya Shpitser

Partial Identification with Noisy Covariates: A Robust Optimization Approach Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael Jordan

Predictive State Propensity Subclassification (PSPS): A Causal Inference Algorithm for Data-Driven Propensity Score Stratification Joseph Kelly, Jing Kong, Georg M. Goerg

Process Independence Testing in Proximal Graphical Event Models Debarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao, Dharmashankar Subramanian

Relational Causal Models with Cycles: Representation and Reasoning Ragib Ahsan, David Arbour, Elena Zheleva

Same Cause; Different Effects in the Brain Mariya Toneva, Jennifer Williams, Anand Bollu, Christoph Dann, Leila Wehbe

Selection, Ignorability and Challenges with Causal Fairness Jake Fawkes, Robin Evans, Dino Sejdinovic

Simple Data Balancing Achieves Competitive Worst-Group-Accuracy Badr Youbi Idrissi, Martin Arjovsky, Mohammad Pezeshki, David Lopez-Paz

Some Reflections on Drawing Causal Inference Using Textual Data: Parallels Between Human Subjects and Organized Texts Bo Zhang, Jiayao Zhang

Towards Efficient Representation Identification in Supervised Learning Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas

Typing Assumptions Improve Identification in Causal Discovery Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sebastien Lachapelle, Alexandre Drouin

VIM: Variational Independent Modules for Video Prediction Rim Assouel, Lluis Castrejon, Aaron Courville, Nicolas Ballas, Yoshua Bengio

Weakly Supervised Discovery of Semantic Attributes Ameen Ali Ali, Tomer Galanti, Evgenii Zheltonozhskii, Chaim Baskin, Lior Wolf