tangxiangru/awesome-causal-inference

Reading list for research topics in causal inference.

Reading List for Topics in Causal Inference.

If there are any areas, papers, and datasets I missed, please let me know!

CVPR 2020

• Unbiased Scene Graph Generation from Biased Training [pdf]
• Visual Commonsense R-CNN [pdf]

AAAI 2020

• Causally Denoise Word Embeddings Using Half-Sibling Regression [pdf]
• A Causal Inference Method for Reducing Gender Bias in Word Embedding Relations [pdf]
• [*] A Simultaneous Discover-Identify Approach to Causal Inference in Linear Models [pdf]
• [*] Explainable Reinforcement Learning Through a Causal Lens [pdf]
• Integrating overlapping datasets using bivariate causal discovery [pdf]
• Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning [pdf]
• CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines [pdf]
• Causal Transfer for Imitation Learning and Decision Making under Sensor-shift
• Recovering Causal Structures from Low-Order Conditional Independencies
• A Bayesian Approach for Estimating Causal Effects From Observational Data
• Estimating Causal Effects using Weighting-based Estimators
• A Calculus for Stochastic Interventions: Causal Effect Identification and Surrogate Experiment
• Multi-Source Causal Feature Selection

ICLR 2020

• [*] Counterfactuals uncover the modular structure of deep generative models [pdf]
• [*] Learning Disentangled Representations for CounterFactual Regression [pdf]
• [*] Learning The Difference That Makes A Difference With Counterfactually-Augmented Data [pdf]
• [*] Explanation by Progressive Exaggeration [pdf]
• [*] Estimating counterfactual treatment outcomes over time through adversarially balanced representations [pdf]
• A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms [pdf]
• [*] Causal Discovery with Reinforcement Learning [pdf]
• CoPhy: Counterfactual Learning of Physical Dynamics [pdf]

ICLR 2020 (rejected)

• [*] A Causal View on Robustness of Neural Networks [pdf]
• MissDeepCausal: causal inference from incomplete data using deep latent variable models [pdf]

FAT* 2020

• Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations [pdf]

AAAI 2019

• [*] Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals [pdf]

ACL 2019

• [*] Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology [pdf]

EMNLP 2019

• [*] Counterfactual Story Reasoning and Generation [pdf]

WWW 2019

• [*] Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality [pdf]

SIGIR 2019

• [*] A General Framework for Counterfactual Learning-to-Rank [pdf]

CIKM 2019

• [*] Scalable Causal Graph Learning through a Deep Neural Network [pdf]

IJCAI 2019

• [*] The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations [pdf]
• [*] Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning [pdf]
• [*] Causal Embeddings for Recommendation: An Extended Abstract [pdf]

CVPR 2019

• [*] Multimodal Explanations by Predicting Counterfactuality in Videos [pdf]

CVPR Workshop 2019

• [*] Question-Conditioned Counterfactual Image Generation for VQA [pdf]

ICLR 2019

• Explaining Image Classifiers by Counterfactual Generation [pdf]
• [*] Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search [pdf]

NIPS 2019

• [*] CXPlain: Causal Explanations for Model Interpretation under Uncertainty [pdf]
• A Game Theoretic Approach to Class-wise Selective Rationalization [pdf]
• Causal Confusion in Imitation Learning

ICML 2019

• Bayesian Counterfactual Risk Minimization [pdf]
• [*] Causal Identification under Markov Equivalence: Completeness Results [pdf]
• [*] Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models [pdf]
• Counterfactual Visual Explanations [pdf]
• [*] Deep Counterfactual Regret Minimization [pdf]
• [*] Learning to Generalize from Sparse and Underspecified Rewards [pdf]
• Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions [pdf]
• Validating Causal Inference Models via Influence Functions [pdf]
• Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness [pdf]

ECCV 2018

• Open Set Learning with Counterfactual Images [pdf]

ICLR 2018

• [*] Calsalgan：Learning Causal Implicit Generative Models with Adversarial Training [pdf]

EMNLP 2017

• [*] A causal framework for explaining the predictions of black box sequence to sequence models [pdf]

Others

• [*] Generating Counterfactual Explanations with Natural Language [pdf] (ICML 2018 Workshop)
• [*] Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling [pdf] (arxiv)
• Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising [pdf] (JMLR 2013)