interventional distribution

Estimating Interventional Outcomes over Time with Causal Normalizing Flow UAI 2026 Estimating Joint Interventional Distributions from Marginal Interventional Data CLeaR 2026 Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders AISTATS 2025 $χ$SPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains UAI 2024 A General Identification Algorithm For Data Fusion Problems Under Systematic Selection UAI 2024 Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand NeurIPS 2024 Identifying General Mechanism Shifts in Linear Causal Representations NeurIPS 2024 Learning Mixtures of Unknown Causal Interventions NeurIPS 2024 Additive Causal Bandits with Unknown Graph ICML 2023 Estimating Causal Effects Identifiable from a Combination of Observations and Experiments NeurIPS 2023 Estimating Joint Treatment Effects by Combining Multiple Experiments ICML 2023 Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributions CLeaR 2023 On the Interventional Kullback-Leibler Divergence CLeaR 2023 Sample Complexity of Distinguishing Cause from Effect AISTATS 2023 Causal forecasting: generalization bounds for autoregressive models UAI 2022 Counterfactual Transportability: A Formal Approach ICML 2022 Efficient interventional distribution learning in the PAC framework AISTATS 2022 VACA: Designing Variational Graph Autoencoders for Causal Queries AAAI 2022 Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models NeurIPS 2021 CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training ICLR 2018 Simplifying Probabilistic Expressions in Causal Inference JMLR 2017 Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs JMLR 2012