Timezone: »
We study causal discovery in a multi-environment setting, in which the functional relations for producing the variables from their direct causes remain the same across environments, while the distribution of exogenous noises may vary. We introduce the idea of using the invariance of the functional relations of the variables to their causes across a set of environments for structure learning. We define a notion of completeness for a causal inference algorithm in this setting and prove the existence of such algorithm by proposing the baseline algorithm. Additionally, we present an alternate algorithm that has significantly improved computational and sample complexity compared to the baseline algorithm. Experiment results show that the proposed algorithm outperforms the other existing algorithms.
Author Information
AmirEmad Ghassami (University of Illinois at Urbana–Champaign)
Saber Salehkaleybar (University of Illinois at Urbana-Champaign)
Negar Kiyavash (Georgia Tech)
Kun Zhang (CMU)
More from the Same Authors
-
2022 : Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors »
Zeyu Tang · Yatong Chen · Yang Liu · Kun Zhang -
2022 : Scalable Causal Discovery with Score Matching »
Francesco Montagna · Nicoletta Noceti · Lorenzo Rosasco · Kun Zhang · Francesco Locatello -
2022 Spotlight: Latent Hierarchical Causal Structure Discovery with Rank Constraints »
Biwei Huang · Charles Jia Han Low · Feng Xie · Clark Glymour · Kun Zhang -
2022 : Kun Zhang: Causal Principles Meet Deep Learning: Successes and Challenges. »
Kun Zhang -
2022 : Kun Zhang: Causal Principles Meet Deep Learning: Successes and Challenges. »
Kun Zhang -
2022 Workshop: Causal Machine Learning for Real-World Impact »
Nick Pawlowski · Jeroen Berrevoets · Caroline Uhler · Kun Zhang · Mihaela van der Schaar · Cheng Zhang -
2022 Poster: On the Identifiability of Nonlinear ICA: Sparsity and Beyond »
Yujia Zheng · Ignavier Ng · Kun Zhang -
2022 Poster: Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models »
Haoyue Dai · Peter Spirtes · Kun Zhang -
2022 Poster: Latent Hierarchical Causal Structure Discovery with Rank Constraints »
Biwei Huang · Charles Jia Han Low · Feng Xie · Clark Glymour · Kun Zhang -
2022 Poster: MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models »
Erdun Gao · Ignavier Ng · Mingming Gong · Li Shen · Wei Huang · Tongliang Liu · Kun Zhang · Howard Bondell -
2022 Poster: Causal Discovery in Linear Latent Variable Models Subject to Measurement Error »
Yuqin Yang · AmirEmad Ghassami · Mohamed Nafea · Negar Kiyavash · Kun Zhang · Ilya Shpitser -
2022 Poster: Unsupervised Image-to-Image Translation with Density Changing Regularization »
Shaoan Xie · Qirong Ho · Kun Zhang -
2022 Poster: Factored Adaptation for Non-Stationary Reinforcement Learning »
Fan Feng · Biwei Huang · Kun Zhang · Sara Magliacane -
2022 Poster: Counterfactual Fairness with Partially Known Causal Graph »
Aoqi Zuo · Susan Wei · Tongliang Liu · Bo Han · Kun Zhang · Mingming Gong -
2022 Poster: Temporally Disentangled Representation Learning »
Weiran Yao · Guangyi Chen · Kun Zhang -
2022 Poster: Truncated Matrix Power Iteration for Differentiable DAG Learning »
Zhen Zhang · Ignavier Ng · Dong Gong · Yuhang Liu · Ehsan Abbasnejad · Mingming Gong · Kun Zhang · Javen Qinfeng Shi -
2020 Workshop: Causal Discovery and Causality-Inspired Machine Learning »
Biwei Huang · Sara Magliacane · Kun Zhang · Danielle Belgrave · Elias Bareinboim · Daniel Malinsky · Thomas Richardson · Christopher Meek · Peter Spirtes · Bernhard Schölkopf -
2018 Poster: Multi-domain Causal Structure Learning in Linear Systems »
AmirEmad Ghassami · Negar Kiyavash · Biwei Huang · Kun Zhang -
2018 Poster: Predictive Approximate Bayesian Computation via Saddle Points »
Yingxiang Yang · Bo Dai · Negar Kiyavash · Niao He -
2017 : Recovering Latent Causal Relations from Times Series Data »
Negar Kiyavash -
2017 Poster: Online Learning for Multivariate Hawkes Processes »
Yingxiang Yang · Jalal Etesami · Niao He · Negar Kiyavash