Book

  • Causal Inference in Statistics: A Primer
  • The Book of Why: The New Science of Cause and Effect
  • Causal Inference: What If
  • Elements of Causal Inference:Foundations and Learning Algorithms
  • Causal Reasoning: Fundamentals and Machine Learning Applications
  • Patterns, Predictions, and Actions: Foundations of Machine Learning
  • Artificial Intelligence and Causal Inference

    • Survey Paper

    1. A Survey on Causal Inference (TKDD2021)
    2. Causal Machine Learning: A Survey and Open Problems ( ArXiv2022)
    3. A Survey of Learning Causality with Data: Problems and Methods (ACM Computing Surveys2020)
    4. Towards Causal Representation Learning ( Proceedings of the IEEE 2021)
    5. Interventional Causal Representation Learning (ArXiv2022)
    6. D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (ACM Computing Surveys2023)
    7. Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond (TACL2022)
    8. Causal Reinforcement Learning: A Survey
    9. A Survey on Causal Reinforcement Learning
    10. Causal Deep Learning
    11. 基于因果建模的强化学习控制: 现状及展望 (自动化学报)

    Causal Reinforcement Learning

    1. Causal Confusion in Imitation Learning (NeurIPS2019)
    2. Causal Imitation Learning with Unobserved Confounders (NeurIPS2020)
    3. Sequential Causal Imitation Learning with Unobserved Confounders (NeurIPS2021)
    4. Learning Human Driving Behaviors with Sequential Causal Imitation Learning (AAAI2022)
    5. Causal Imitation Learning via Inverse Reinforcement Learning (ICLR2023 Under Review)
    6. Causal Imitation Learning under Temporally Correlated Noise (ICML2022)
    7. Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning (ICML2022)
    8. Invariant Causal Representation Learning for Generalization in Imitation and Reinforcement Learning (Workshop@ICLR2022)
    9. Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning (NeurIPS2021)
    10. Provably Efficient Causal Reinforcement Learning with Confounded Observational Data (NeurIPS2021)
    11. Invariant Causal Imitation Learning for Generalizable Policies (NeurIPS2021)
    12. Training a Resilient Q-network against Observational Interference (AAAI2022)
    13. Causal Multi-Agent Reinforcement Learning: Review and Open Problems (arXiv2021)

    Weekly Reading

    Date
    Reporter
    Title/Papers
    Conference/Journal
    27 Mar. Tang [1].Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification
    [2].Introspective Distillation for Robust Question Answering     		AAAI2022

    NeurIPS2021
    20 Mar. Zhang

    Tian    		 [3].融合情境信息的趋势预测建模方法研究
    [4].Sample-efficient reinforcement learning via counterfactual-based data augmentation     		PhD Thesis of USTC

    OfflineRL@NeurIPS2021
    12 Mar. Tang [5].Causal Inference Look At Unsupervised Video Anomaly Detection
    [6].A Causal Debiasing Framework for Unsupervised Salient Object Detection
    		AAAI2022

    AAAI2022
    5 Apr. Tang [7].Debiasing NLU models via Causal Intervention and Counterfactual Reasoning AAAI2022
    24 Apr. Tian [8].GIKT: a graph-based interaction model for knowledge tracing ECML-PKDD2020
    30 Apr. Tang [9].Causal Inference with Knowledge Distilling and Curriculum Learning for Unbiased VQA TOMCCAP2022
    8 May Zhang [10].Causal Hidden Markov Model for Time Series Disease Forecasting CVPR2021
    15 May Tian [11].CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models CVPR2021
    24 May Tang [12].Causal Representation Learning for Out-of-Distribution Recommendation
    [13].Invariant Causal Imitation Learning for Generalizable Policie     		WWW2022
    NeurIPS2021
    19 Jun. Tang Offline Reinforcement Learning & Multi-Task Learning
    26 Jun. Tian [14].Causal Imitation Learning With Unobserved Confounders
    NeurIPS2020
    9 Jul. Tang [15].Deconfounded Visual Grounding
    AAAI2022

    Dataset

    IL Dataset