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Conference Track

Here, we track the top conference papers in our subject.

NIPS

  1. Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera
  2. Causal Intervention for Weakly-Supervised Semantic Segmentation Dong Zhang, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun
  3. Deep Structural Causal Models for Tractable Counterfactual Inference Nick Pawlowski, Daniel Coelho de Castro, Ben Glocker
  4. Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability Christopher Frye, Colin Rowat, Ilya Feige
  5. Learning to search efficiently for causally near-optimal treatments Samuel Håkansson, Viktor Lindblom, Omer Gottesman, Fredrik D. Johansson
  6. A causal view of compositional zero-shot recognition Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik
  7. CASTLE: Regularization via Auxiliary Causal Graph Discovery Trent Kyono, Yao Zhang, Mihaela van der Schaar
  8. Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect Kaihua Tang, Jianqiang Huang, Hanwang Zhang
  9. Causal analysis of Covid-19 Spread in Germany Atalanti Mastakouri, Bernhard Schölkopf
  10. Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen
  11. Causal Estimation with Functional Confounders Aahlad Puli, Adler Perotte, Rajesh Ranganath
  12. Generative causal explanations of black-box classifiers Matthew O'Shaughnessy, Gregory Canal, Marissa Connor, Christopher Rozell, Mark Davenport
  13. Towards practical differentially private causal graph discovery Lun Wang, Qi Pang, Dawn Song
  14. Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks Junsouk Choi, Robert Chapkin, Yang Ni
  15. Multi-task Causal Learning with Gaussian Processes Virginia Aglietti, Theodoros Damoulas, Mauricio Álvarez, Javier González
  16. Towards Scalable Bayesian Learning of Causal DAGs Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto
  17. General Control Functions for Causal Effect Estimation from IVs Aahlad Puli, Rajesh Ranganath
  18. COT-GAN: Generating Sequential Data via Causal Optimal Transport Tianlin Xu, Li Kevin Wenliang, Michael Munn, Beatrice Acciaio
  19. Causal Discovery in Physical Systems from Videos Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, Animesh Garg
  20. Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim
  21. A polynomial-time algorithm for learning nonparametric causal graphs Ming Gao, Yi Ding, Bryon Aragam
  22. Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal
  23. Causal Imitation Learning With Unobserved Confounders Junzhe Zhang, Daniel Kumor, Elias Bareinboim
  24. High-recall causal discovery for autocorrelated time series with latent confounders Andreas Gerhardus, Jakob Runge
  25. Learning Causal Effects via Weighted Empirical Risk Minimization Yonghan Jung, Jin Tian, Elias Bareinboim
  26. Entropic Causal Inference: Identifiability and Finite Sample Results Spencer Compton, Murat Kocaoglu, Kristjan Greenewald, Dmitriy Katz
  27. Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang
  28. Active Invariant Causal Prediction: Experiment Selection through Stability Juan L. Gamella, Christina Heinze-Deml
  29. Applications of Common Entropy for Causal Inference Murat Kocaoglu, Sanjay Shakkottai, Alexandros G. Dimakis, Constantine Caramanis, Sriram Vishwanath
  30. Active Structure Learning of Causal DAGs via Directed Clique Trees Chandler Squires, Sara Magliacane, Kristjan Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam
  31. Differentiable Causal Discovery from Interventional Data Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin

ICML

  1. Tian-Zuo Wang 1 Xi-Zhu Wu 1 Sheng-Jun Huang 2 Zhi-Hua Zhou 1, Cost-effectively Identifying Causal Effects When Only Response Variable is Observable.
  2. Miller, John ; Milli, Smitha ; Hardt, Moritz, Strategic Classification is Causal Modeling in Disguise.
  3. Tagasovska, Natasa ; Chavez-Demoulin, Valérie ; Vatter, Thibault, Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery
  4. Nathan Kallus, DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training
  5. Basil Saeed 1 Snigdha Panigrahi 2 Caroline Uhler 1, Causal Structure Discovery from Distributions Arising from Mixtures of DAGs

IJCAI

  1. Knowledge Enhanced Event Causality Identification with Mention Masking GeneralizationsJian Liu, Yubo Chen, Jun Zhao*
  2. Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence ModellingDaniel Stoller, Mi Tian, Sebastian Ewert, Simon Dixon
  3. Variational Learning of Bayesian Neural Networks via Bayesian Dark KnowledgeGehui Shen, Xi Chen, Zhihong Deng
  4. Learning Bayesian Networks Under Sparsity Constraints: A Parameterized Complexity AnalysisNiels Grüttemeier, Christian Komusiewicz*
  5. Relation-Based Counterfactual Explanations for Bayesian Network Classifiers Emanuele Albini, Antonio Rago, Pietro Baroni, Francesca Toni
  6. BaKer-Nets: Bayesian Random Kernel Mapping Networks Hui Xue, Zheng-Fan Wu

AAAI

  1. Multi-label Causal Feature Selection Xingyu Wu (University of Science and Technology of China); Bingbing Jiang (School of Computer Science and Technology, University of Science and Technology of China); Kui Yu (School of Computer and Information, Hefei University of Technology); Huanhuan Chen (School of Computer Science and Technology, University of Science and Technology of China)*; Chunyan Miao (NTU)
  2. Causal Transfer for Imitation Learning and Decision Making under Sensor-shift Jalal Etesami (Bosch Research Center for AI, Germany); Philipp Geiger (Bosch Center for Artificial Intelligence)*
  3. Recovering Causal Structures from Low-Order Conditional Independencies Marcel Wienöbst (Universität zu Lübeck)*; Maciej Liskiewicz (Universität zu Lübeck)
  4. A Causal Inference Method for Reducing Gender Bias in Word Embedding Relations Zekun Yang (City University of Hong Kong)*; Juan Feng (City University of Hong Kong)
  5. A Simultaneous Discover-Identify Approach to Causal Inference in Linear Models Chi Zhang (University of California, Los Angeles)*; Bryant Chen (IBM Research AI); Judea Pearl (University of California, Los Angeles)
  6. : A Bayesian Approach for Estimating Causal Effects from Observational Data, Johan Pensar (University of Helsinki)*; Topi Talvitie (University of Helsinki); Antti Hyttinen (University of Helsinki); Mikko Koivisto (University of Helsinki)
  7. Explainable Reinforcement Learning Through a Causal Lens Prashan Madumal (University of Melbourne)*; Tim Miller (University of Melbourne); Liz Sonenberg (University of Melbourne); Frank Vetere (University of Melbourne)
  8. Integrating Overlapping Datasets Using Bivariate Causal Discovery Anish Dhir (Babylon Health); Ciarán Lee (University College London and Babylon Health)*
  9. : Estimating Causal Effects Using Weighting-Based Estimators Yonghan Jung (Purdue University)*; Jin Tian (Iowa State University); Elias Bareinboim (Columbia University)
  10. Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets Biwei Huang (CMU)*; Kun Zhang (Carnegie Mellon University); Mingming Gong (University of Melbourne); Clark Glymour (Carnegie Mellon University)
  11. Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning Mark Edmonds (UCLA)*; Xiaojian Ma (University of California, Los Angeles); Siyuan Qi (UCLA); Yixin Zhu (UCLA); Hongjing Lu (UCLA); Song-Chun Zhu (UCLA)
  12. A Calculus for Stochastic Interventions: Causal Effect Identification and Surrogate Experiments Juan Correa (Columbia University)*; Elias Bareinboim (Columbia University)
  13. Probabilistic Reasoning across the Causal Hierarchy Duligur Ibeling (Stanford University)*; Thomas Icard (Stanford University)