causal inference

Counterfactual Invariance to Spurious Correlations in Text Classification NeurIPS 2021 Counterfactual Maximum Likelihood Estimation for Training Deep Networks NeurIPS 2021 Counterfactual Representation Learning with Balancing Weights AISTATS 2021 Counterfactual VQA: A Cause-Effect Look at Language Bias CVPR 2021 DAGSurv: Directed Ayclic Graph Based Survival Analysis Using Deep Neural Networks ACML 2021 De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention ACL 2021 DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks NeurIPS 2021 De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation ACL 2021 Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings NeurIPS 2021 Deeply-Debiased Off-Policy Interval Estimation ICML 2021 Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation NeurIPS 2021 Dependency in DAG models with hidden variables UAI 2021 Dependent Multi-Task Learning with Causal Intervention for Image Captioning IJCAI 2021 Designing Transportable Experiments Under S-admissability AISTATS 2021 DiBS: Differentiable Bayesian Structure Learning NeurIPS 2021 Differentiable Causal Discovery Under Unmeasured Confounding AISTATS 2021 Disentangling mixtures of unknown causal interventions UAI 2021 Distilling Causal Effect of Data in Class-Incremental Learning CVPR 2021 Does Invariant Risk Minimization Capture Invariance? AISTATS 2021 Double/Debiased Machine Learning for Dynamic Treatment Effects NeurIPS 2021 Double Machine Learning Density Estimation for Local Treatment Effects with Instruments NeurIPS 2021 Efficient Balanced Treatment Assignments for Experimentation AISTATS 2021 Efficient Online Estimation of Causal Effects by Deciding What to Observe NeurIPS 2021 Enhancing Model Robustness and Fairness with Causality: A Regularization Approach EMNLP 2021 Equivalent Causal Models AAAI 2021