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Papers
Nonlinear Causal Discovery with Latent Confounders
ICML 2023
Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound
PGM 2022
Efficient Neural Causal Discovery without Acyclicity Constraints
ICLR 2022
Greedy equivalence search in the presence of latent confounders
UAI 2022
Latent Hierarchical Causal Structure Discovery with Rank Constraints
NeurIPS 2022
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning
ICLR 2022
Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge
NeurIPS 2022
Actively Identifying Causal Effects with Latent Variables Given Only Response Variable Observable
NeurIPS 2021
Causal Modeling with Stochastic Confounders
AISTATS 2021
Collaborative Causal Discovery with Atomic Interventions
NeurIPS 2021
Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias
NeurIPS 2021
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders
AISTATS 2021
Statistical Undecidability in Linear, Non-Gaussian Causal Models in the Presence of Latent Confounders
NeurIPS 2021
Causal Inference using Gaussian Processes with Structured Latent Confounders
ICML 2020
High-recall causal discovery for autocorrelated time series with latent confounders
NeurIPS 2020
RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders
AISTATS 2020
Structure Learning for Cyclic Linear Causal Models
UAI 2020
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias
UAI 2019
Estimating Causal Effects of Tone in Online Debates
IJCAI 2019
Object Conditioning for Causal Inference
UAI 2019
Policy Evaluation with Latent Confounders via Optimal Balance
NeurIPS 2019
Contextual Bandits with Latent Confounders: An NMF Approach
AISTATS 2017
Bayesian Estimation of Causal Direction in Acyclic Structural Equation Models with Individual-specific Confounder Variables and Non-Gaussian Distributions
JMLR 2014