@inproceedings{langley00,
 author    = {P. Langley},
 title     = {Crafting Papers on Machine Learning},
 year      = {2000},
 pages     = {1207--1216},
 editor    = {Pat Langley},
 booktitle     = {Proceedings of the 17th International Conference
              on Machine Learning (ICML 2000)},
 address   = {Stanford, CA},
 publisher = {Morgan Kaufmann}
}

@TechReport{mitchell80,
  author = 	 "T. M. Mitchell",
  title = 	 "The Need for Biases in Learning Generalizations",
  institution =  "Computer Science Department, Rutgers University",
  year = 	 "1980",
  address =	 "New Brunswick, MA",
}

@phdthesis{kearns89,
  author = {M. J. Kearns},
  title =  {Computational Complexity of Machine Learning},
  school = {Department of Computer Science, Harvard University},
  year =   {1989}
}

@Book{MachineLearningI,
  editor = 	 "R. S. Michalski and J. G. Carbonell and T.
		  M. Mitchell",
  title = 	 "Machine Learning: An Artificial Intelligence
		  Approach, Vol. I",
  publisher = 	 "Tioga",
  year = 	 "1983",
  address =	 "Palo Alto, CA"
}

@Book{DudaHart2nd,
  author =       "R. O. Duda and P. E. Hart and D. G. Stork",
  title =        "Pattern Classification",
  publisher =    "John Wiley and Sons",
  edition =      "2nd",
  year =         "2000"
}

@misc{anonymous,
  title= {Suppressed for Anonymity},
  author= {Author, N. N.},
  year= {2021}
}

@InCollection{Newell81,
  author =       "A. Newell and P. S. Rosenbloom",
  title =        "Mechanisms of Skill Acquisition and the Law of
                  Practice", 
  booktitle =    "Cognitive Skills and Their Acquisition",
  pages =        "1--51",
  publisher =    "Lawrence Erlbaum Associates, Inc.",
  year =         "1981",
  editor =       "J. R. Anderson",
  chapter =      "1",
  address =      "Hillsdale, NJ"
}


@Article{Samuel59,
  author = 	 "A. L. Samuel",
  title = 	 "Some Studies in Machine Learning Using the Game of
		  Checkers",
  journal =	 "IBM Journal of Research and Development",
  year =	 "1959",
  volume =	 "3",
  number =	 "3",
  pages =	 "211--229"
}



@inproceedings{maes2005identification,
  title={Identification of Causal Effects in Multi-Agent Causal Models.},
  author={Maes, Sam and Meganck, Stijn and Manderick, Bernard},
  booktitle={Artificial Intelligence and Applications},
  pages={178--182},
  year={2005}
}


@article{francis2021towards,
  title={Towards causal federated learning for enhanced robustness and privacy},
  author={Francis, Sreya and Tenison, Irene and Rish, Irina},
  journal={arXiv preprint arXiv:2104.06557},
  year={2021}
}


@inproceedings{zhao2018dynamic,
  title={Dynamic conditional networks for few-shot learning},
  author={Zhao, Fang and Zhao, Jian and Yan, Shuicheng and Feng, Jiashi},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={19--35},
  year={2018}
}

@article{sinha2021d2c,
  title={D2c: Diffusion-decoding models for few-shot conditional generation},
  author={Sinha, Abhishek and Song, Jiaming and Meng, Chenlin and Ermon, Stefano},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={12533--12548},
  year={2021}
}

@article{buchholz2023learning,
  title={Learning Linear Causal Representations from Interventions under General Nonlinear Mixing},
  author={Buchholz, Simon and Rajendran, Goutham and Rosenfeld, Elan and Aragam, Bryon and Sch{\"o}lkopf, Bernhard and Ravikumar, Pradeep},
  journal={arXiv preprint arXiv:2306.02235},
  year={2023}
}



@article{wang2022diffusion,
  title={Diffusion-gan: Training gans with diffusion},
  author={Wang, Zhendong and Zheng, Huangjie and He, Pengcheng and Chen, Weizhu and Zhou, Mingyuan},
  journal={arXiv preprint arXiv:2206.02262},
  year={2022}
}

@inproceedings{strobl2023sample,
  title={Sample-specific root causal inference with latent variables},
  author={Strobl, Eric and Lasko, Thomas A},
  booktitle={Conference on Causal Learning and Reasoning},
  pages={895--915},
  year={2023},
  organization={PMLR}
}

@article{vo2022adaptive,
  title={An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects},
  author={Vo, Thanh Vinh and Bhattacharyya, Arnab and Lee, Young and Leong, Tze-Yun},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={24459--24473},
  year={2022}
}


@article{hill2011bayesian,
  title={Bayesian nonparametric modeling for causal inference},
  author={Hill, Jennifer L},
  journal={Journal of Computational and Graphical Statistics},
  volume={20},
  number={1},
  pages={217--240},
  year={2011},
  publisher={Taylor \& Francis}
}


@inproceedings{jaber2018graphical,
  title={A graphical criterion for effect identification in equivalence classes of causal diagrams},
  author={Jaber, Amin and Zhang, Jiji and Bareinboim, Elias},
  booktitle={Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence},
  year={2018}
}


@inproceedings{zhang2020simultaneous,
  title={A simultaneous discover-identify approach to causal inference in linear models},
  author={Zhang, Chi and Chen, Bryant and Pearl, Judea},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={06},
  pages={10318--10325},
  year={2020}
}


@article{zhu2019causal,
  title={Causal discovery with reinforcement learning},
  author={Zhu, Shengyu and Ng, Ignavier and Chen, Zhitang},
  journal={arXiv preprint arXiv:1906.04477},
  year={2019}
}



@inproceedings{sauter2024core,
  title={CORE Towards Scalable and Efficient Causal Discovery with Reinforcement Learning},
  author={Sauter, Andreas and Botteghi, Nicolo and Acar, Erman and Plaat, Aske},
  booktitle={Proceedings of the 2024 International Conference on Autonomous Agents and Multiagent Systems},
  year={2024}
}



@inproceedings{bhattacharya2021differentiable,
  title={Differentiable causal discovery under unmeasured confounding},
  author={Bhattacharya, Rohit and Nagarajan, Tushar and Malinsky, Daniel and Shpitser, Ilya},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={2314--2322},
  year={2021},
  organization={PMLR}
}


@phdthesis{bhattacharya2021pragmatic,
  title={Pragmatic Causal Inference},
  author={Bhattacharya, Rohit and others},
  year={2021},
  school={Johns Hopkins University}
}




@article{ke2023neural,
  title={Neural Causal Structure Discovery from Interventions},
  author={Ke, Nan Rosemary and Bilaniuk, Olexa and Goyal, Anirudh and Bauer, Stefan and Larochelle, Hugo and Sch{\"o}lkopf, Bernhard and Mozer, Michael Curtis and Pal, Christopher and Bengio, Yoshua},
  journal={Transactions on Machine Learning Research},
  year={2023}
}


@article{brouillard2020differentiable,
  title={Differentiable causal discovery from interventional data},
  author={Brouillard, Philippe and Lachapelle, S{\'e}bastien and Lacoste, Alexandre and Lacoste-Julien, Simon and Drouin, Alexandre},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={21865--21877},
  year={2020}
}


@article{vowels2022d,
  title={D’ya like dags? a survey on structure learning and causal discovery},
  author={Vowels, Matthew J and Camgoz, Necati Cihan and Bowden, Richard},
  journal={ACM Computing Surveys},
  volume={55},
  number={4},
  pages={1--36},
  year={2022},
  publisher={ACM New York, NY}
}

@article{montagna2023assumption,
  title={Assumption violations in causal discovery and the robustness of score matching},
  author={Montagna, Francesco and Mastakouri, Atalanti A and Eulig, Elias and Noceti, Nicoletta and Rosasco, Lorenzo and Janzing, Dominik and Aragam, Bryon and Locatello, Francesco},
  journal={arXiv preprint arXiv:2310.13387},
  year={2023}
}




@article{feder2023causal,
  title={Causal-structure Driven Augmentations for Text OOD Generalization},
  author={Feder, Amir and Wald, Yoav and Shi, Claudia and Saria, Suchi and Blei, David},
  journal={arXiv preprint arXiv:2310.12803},
  year={2023}
}




@article{bengio2023gflownet,
  title={Gflownet foundations},
  author={Bengio, Yoshua and Lahlou, Salem and Deleu, Tristan and Hu, Edward J and Tiwari, Mo and Bengio, Emmanuel},
  journal={Journal of Machine Learning Research},
  volume={24},
  number={210},
  pages={1--55},
  year={2023}
}


@inproceedings{atanackovic2023dyngfn,
  title={DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets},
  author={Atanackovic, Lazar and Tong, Alexander and Wang, Bo and Lee, Leo J and Bengio, Yoshua and Hartford, Jason},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023}
}



@article{nguyen2023causal,
  title={Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems},
  author={Nguyen, Trang and Tong, Alexander and Madan, Kanika and Bengio, Yoshua and Liu, Dianbo},
  journal={arXiv preprint arXiv:2310.03579},
  year={2023}
}





@article{coelho2020causality,
  title={Causality matters in medical imaging},
  author={Coelho De Castro, D and Walker, Ian and Glocker, Ben},
  year={2020},
  publisher={Nature Research (part of Springer Nature)}
}



@misc{christopher2024projected,
      title={Projected Generative Diffusion Models for Constraint Satisfaction}, 
      author={Jacob K Christopher and Stephen Baek and Ferdinando Fioretto},
      year={2024},
      eprint={2402.03559},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}



@phdthesis{gultchin2023casual,
  title={Casual and trustworthy machine learning: methods and applications},
  author={Gultchin, Limor},
  year={2023},
  school={University of Oxford}
}



@misc{du2024compositional,
      title={Compositional Generative Modeling: A Single Model is Not All You Need}, 
      author={Yilun Du and Leslie Kaelbling},
      year={2024},
      eprint={2402.01103},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}





@inproceedings{liu2022compositional,
  title={Compositional visual generation with composable diffusion models},
  author={Liu, Nan and Li, Shuang and Du, Yilun and Torralba, Antonio and Tenenbaum, Joshua B},
  booktitle={European Conference on Computer Vision},
  pages={423--439},
  year={2022},
  organization={Springer}
}


@article{yang2023compositional,
  title={Compositional Diffusion-Based Continuous Constraint Solvers},
  author={Yang, Zhutian and Mao, Jiayuan and Du, Yilun and Wu, Jiajun and Tenenbaum, Joshua B and Lozano-P{\'e}rez, Tom{\'a}s and Kaelbling, Leslie Pack},
  journal={arXiv preprint arXiv:2309.00966},
  year={2023}
}


@misc{liu2024rethinking,
      title={Rethinking Machine Unlearning for Large Language Models}, 
      author={Sijia Liu and Yuanshun Yao and Jinghan Jia and Stephen Casper and Nathalie Baracaldo and Peter Hase and Xiaojun Xu and Yuguang Yao and Hang Li and Kush R. Varshney and Mohit Bansal and Sanmi Koyejo and Yang Liu},
      year={2024},
      eprint={2402.08787},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}


@misc{singh2023human,
      title={Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models}, 
      author={Avi Singh and John D. Co-Reyes and Rishabh Agarwal and Ankesh Anand and Piyush Patil and Xavier Garcia and Peter J. Liu and James Harrison and Jaehoon Lee and Kelvin Xu and Aaron Parisi and Abhishek Kumar and Alex Alemi and Alex Rizkowsky and Azade Nova and Ben Adlam and Bernd Bohnet and Gamaleldin Elsayed and Hanie Sedghi and Igor Mordatch and Isabelle Simpson and Izzeddin Gur and Jasper Snoek and Jeffrey Pennington and Jiri Hron and Kathleen Kenealy and Kevin Swersky and Kshiteej Mahajan and Laura Culp and Lechao Xiao and Maxwell L. Bileschi and Noah Constant and Roman Novak and Rosanne Liu and Tris Warkentin and Yundi Qian and Yamini Bansal and Ethan Dyer and Behnam Neyshabur and Jascha Sohl-Dickstein and Noah Fiedel},
      year={2023},
      eprint={2312.06585},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}


@article{kocaoglu2024characterization,
  title={Characterization and Learning of Causal Graphs with Small Conditioning Sets},
  author={Kocaoglu, Murat},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}






@article{xia2021causal,
  title={The causal-neural connection: Expressiveness, learnability, and inference},
  author={Xia, Kevin and Lee, Kai-Zhan and Bengio, Yoshua and Bareinboim, Elias},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={10823--10836},
  year={2021}
}


@inproceedings{le2021analysis,
  title={An analysis of the adaptation speed of causal models},
  author={Le Priol, R{\'e}mi and Babanezhad, Reza and Bengio, Yoshua and Lacoste-Julien, Simon},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={775--783},
  year={2021},
  organization={PMLR}
}


@inproceedings{lin2023adaptation,
  title={Adaptation Speed Analysis for Fairness-aware Causal Models},
  author={Lin, Yujie and Zhao, Chen and Shao, Minglai and Zhao, Xujiang and Chen, Haifeng},
  booktitle={Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
  pages={1421--1430},
  year={2023}
}


@ARTICLE{9839432,
  author={Li, Haoyang and Wang, Xin and Zhang, Ziwei and Zhu, Wenwu},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  title={OOD-GNN: Out-of-Distribution Generalized Graph Neural Network}, 
  year={2023},
  volume={35},
  number={7},
  pages={7328-7340},
  keywords={Testing;Decorrelation;Correlation;Training;Random variables;Predictive models;Data models;Graph representation learning;graph neural networks;out-of-distribution generalization},
  doi={10.1109/TKDE.2022.3193725}}



@article{zhou2021training,
  title={Training on test data with bayesian adaptation for covariate shift},
  author={Zhou, Aurick and Levine, Sergey},
  journal={arXiv preprint arXiv:2109.12746},
  year={2021}
}

@inproceedings{meganck2006learning,
  title={Learning Semi-Markovian Causal Models using Experiments.},
  author={Meganck, Stijn and Maes, Sam and Leray, Philippe and Manderick, Bernard and Rouen, INSA and St Etienne du Rouvray, France},
  booktitle={Probabilistic Graphical Models},
  pages={195--206},
  year={2006},
  organization={Citeseer}
}



@article{wang2020tent,
  title={Tent: Fully test-time adaptation by entropy minimization},
  author={Wang, Dequan and Shelhamer, Evan and Liu, Shaoteng and Olshausen, Bruno and Darrell, Trevor},
  journal={arXiv preprint arXiv:2006.10726},
  year={2020}
}

@inproceedings{mao2022causal,
  title={Causal transportability for visual recognition},
  author={Mao, Chengzhi and Xia, Kevin and Wang, James and Wang, Hao and Yang, Junfeng and Bareinboim, Elias and Vondrick, Carl},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7521--7531},
  year={2022}
}

@article{sheth2022domain,
  title={Domain Generalization--A Causal Perspective},
  author={Sheth, Paras and Moraffah, Raha and Candan, K Sel{\c{c}}uk and Raglin, Adrienne and Liu, Huan},
  journal={arXiv preprint arXiv:2209.15177},
  year={2022}
}

@inproceedings{wang2022continual,
  title={Continual test-time domain adaptation},
  author={Wang, Qin and Fink, Olga and Van Gool, Luc and Dai, Dengxin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7201--7211},
  year={2022}
}


@article{grandvalet2004semi,
  title={Semi-supervised learning by entropy minimization},
  author={Grandvalet, Yves and Bengio, Yoshua},
  journal={Advances in neural information processing systems},
  volume={17},
  year={2004}
}

@article{zhang2022memo,
  title={Memo: Test time robustness via adaptation and augmentation},
  author={Zhang, Marvin and Levine, Sergey and Finn, Chelsea},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={38629--38642},
  year={2022}
}

@inproceedings{niu2022efficient,
  title={Efficient test-time model adaptation without forgetting},
  author={Niu, Shuaicheng and Wu, Jiaxiang and Zhang, Yifan and Chen, Yaofo and Zheng, Shijian and Zhao, Peilin and Tan, Mingkui},
  booktitle={International conference on machine learning},
  pages={16888--16905},
  year={2022},
  organization={PMLR}
}

@inproceedings{valanarasu2024fly,
  title={On-the-fly test-time adaptation for medical image segmentation},
  author={Valanarasu, Jeya Maria Jose and Guo, Pengfei and Vibashan, VS and Patel, Vishal M},
  booktitle={Medical Imaging with Deep Learning},
  pages={586--598},
  year={2024},
  organization={PMLR}
}

@inproceedings{ye2022ood,
  title={Ood-bench: Quantifying and understanding two dimensions of out-of-distribution generalization},
  author={Ye, Nanyang and Li, Kaican and Bai, Haoyue and Yu, Runpeng and Hong, Lanqing and Zhou, Fengwei and Li, Zhenguo and Zhu, Jun},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7947--7958},
  year={2022}
}


@article{van2019three,
  title={Three scenarios for continual learning},
  author={Van de Ven, Gido M and Tolias, Andreas S},
  journal={arXiv preprint arXiv:1904.07734},
  year={2019}
}


@article{mian2024information,
  title={An Information Theoretic Framework for Continual Learning of Causal Networks.},
  author={Mian, Osman and Mameche, Sarah},
  year={2024}
}



@inproceedings{mian2023nothing,
  title={Nothing but regrets—privacy-preserving federated causal discovery},
  author={Mian, Osman and Kaltenpoth, David and Kamp, Michael and Vreeken, Jilles},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={8263--8278},
  year={2023},
  organization={PMLR}
}

@inproceedings{pena2016learning,
  title={Learning acyclic directed mixed graphs from observations and interventions},
  author={Pe{\~n}a, Jose M},
  booktitle={Conference on Probabilistic Graphical Models},
  pages={392--402},
  year={2016},
  organization={PMLR}
}


@book{turnbull2024constraint,
  title={Constraint-Score Hybrid Structure Learning for Directed Acyclic Graphs With Latent Confounders},
  author={Turnbull, Steven},
  year={2024},
  publisher={University of California, Los Angeles}
}



@article{jones2024causal,
  title={A causal perspective on dataset bias in machine learning for medical imaging},
  author={Jones, Charles and Castro, Daniel C and De Sousa Ribeiro, Fabio and Oktay, Ozan and McCradden, Melissa and Glocker, Ben},
  journal={Nature Machine Intelligence},
  pages={1--9},
  year={2024},
  publisher={Nature Publishing Group UK London}
}


@article{sturma2024unpaired,
  title={Unpaired multi-domain causal representation learning},
  author={Sturma, Nils and Squires, Chandler and Drton, Mathias and Uhler, Caroline},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}



@article{li2024causal,
  title={Causal discovery from observational and interventional data across multiple environments},
  author={Li, Adam and Jaber, Amin and Bareinboim, Elias},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}


@article{ke2022learning,
  title={Learning to induce causal structure},
  author={Ke, Nan Rosemary and Chiappa, Silvia and Wang, Jane and Goyal, Anirudh and Bornschein, Jorg and Rey, Melanie and Weber, Theophane and Botvinic, Matthew and Mozer, Michael and Rezende, Danilo Jimenez},
  journal={arXiv preprint arXiv:2204.04875},
  year={2022}
}


@article{ke2023discogen,
  title={Discogen: Learning to discover gene regulatory networks},
  author={Ke, Nan Rosemary and Dunn, Sara-Jane and Bornschein, Jorg and Chiappa, Silvia and Rey, Melanie and Lespiau, Jean-Baptiste and Cassirer, Albin and Wang, Jane and Weber, Theophane and Barrett, David and others},
  journal={arXiv preprint arXiv:2304.05823},
  year={2023}
}



@article{atanackovic2024dyngfn,
  title={DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets},
  author={Atanackovic, Lazar and Tong, Alexander and Wang, Bo and Lee, Leo J and Bengio, Yoshua and Hartford, Jason S},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}



@article{ke2021systematic,
  title={Systematic evaluation of causal discovery in visual model based reinforcement learning},
  author={Ke, Nan Rosemary and Didolkar, Aniket and Mittal, Sarthak and Goyal, Anirudh and Lajoie, Guillaume and Bauer, Stefan and Rezende, Danilo and Bengio, Yoshua and Mozer, Michael and Pal, Christopher},
  journal={arXiv preprint arXiv:2107.00848},
  year={2021}
}


@article{zhang2024causal,
  title={Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment},
  author={Zhang, Congzhi and Zhang, Linhai and Zhou, Deyu and Xu, Guoqiang},
  journal={arXiv preprint arXiv:2403.02738},
  year={2024}
}


@article{ke2019learning,
  title={Learning neural causal models from unknown interventions},
  author={Ke, Nan Rosemary and Bilaniuk, Olexa and Goyal, Anirudh and Bauer, Stefan and Larochelle, Hugo and Sch{\"o}lkopf, Bernhard and Mozer, Michael C and Pal, Chris and Bengio, Yoshua},
  journal={arXiv preprint arXiv:1910.01075},
  year={2019}
}


@inproceedings{perkovic2020identifying,
  title={Identifying causal effects in maximally oriented partially directed acyclic graphs},
  author={Perkovic, Emilija},
  booktitle={Conference on Uncertainty in Artificial Intelligence},
  pages={530--539},
  year={2020},
  organization={PMLR}
}


@inproceedings{yoon2018gain,
  title={Gain: Missing data imputation using generative adversarial nets},
  author={Yoon, Jinsung and Jordon, James and Schaar, Mihaela},
  booktitle={International conference on machine learning},
  pages={5689--5698},
  year={2018},
  organization={PMLR}
}

@article{ibrahim2024semi,
  title={Semi-Supervised Learning for Deep Causal Generative Models},
  author={Ibrahim, Yasin and Warr, Hermione and Kamnitsas, Konstantinos},
  journal={arXiv preprint arXiv:2403.18717},
  year={2024}
}



@article{monteiro2023measuring,
  title={Measuring axiomatic soundness of counterfactual image models},
  author={Monteiro, Miguel and Ribeiro, Fabio De Sousa and Pawlowski, Nick and Castro, Daniel C and Glocker, Ben},
  journal={arXiv preprint arXiv:2303.01274},
  year={2023}
}

@article{lee2023cxr,
  title={CXR-LLaVA: Multimodal Large Language Model for Interpreting Chest X-ray Images},
  author={Lee, Seowoo and Youn, Jiwon and Kim, Mansu and Yoon, Soon Ho},
  journal={arXiv preprint arXiv:2310.18341},
  year={2023}
}


@article{roschewitz2024counterfactual,
  title={Counterfactual contrastive learning: robust representations via causal image synthesis},
  author={Roschewitz, Melanie and Ribeiro, Fabio De Sousa and Xia, Tian and Khara, Galvin and Glocker, Ben},
  journal={arXiv preprint arXiv:2403.09605},
  year={2024}
}



@book{pearl2009causality,
  title={Causality},
  author={Pearl, J.},
  isbn={9781139643986},
  url={https://books.google.com/books?id=LLkhAwAAQBAJ},
  year={2009},
  publisher={Cambridge University Press}
}

@misc{ho2022classifierfree,
      title={Classifier-Free Diffusion Guidance}, 
      author={Jonathan Ho and Tim Salimans},
      year={2022},
      eprint={2207.12598},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{dhariwal2021diffusion,
      title={Diffusion Models Beat GANs on Image Synthesis}, 
      author={Prafulla Dhariwal and Alex Nichol},
      year={2021},
      eprint={2105.05233},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{song2022denoising,
      title={Denoising Diffusion Implicit Models}, 
      author={Jiaming Song and Chenlin Meng and Stefano Ermon},
      year={2022},
      eprint={2010.02502},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{ho2020denoising,
      title={Denoising Diffusion Probabilistic Models}, 
      author={Jonathan Ho and Ajay Jain and Pieter Abbeel},
      year={2020},
      eprint={2006.11239},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{kim2022diffusionclip,
      title={DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation}, 
      author={Gwanghyun Kim and Taesung Kwon and Jong Chul Ye},
      year={2022},
      eprint={2110.02711},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{yang2023zeroshot,
      title={Zero-Shot Contrastive Loss for Text-Guided Diffusion Image Style Transfer}, 
      author={Serin Yang and Hyunmin Hwang and Jong Chul Ye},
      year={2023},
      eprint={2303.08622},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{brooks2023instructpix2pix,
      title={InstructPix2Pix: Learning to Follow Image Editing Instructions}, 
      author={Tim Brooks and Aleksander Holynski and Alexei A. Efros},
      year={2023},
      eprint={2211.09800},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{sanchez2022diffusion,
      title={Diffusion Causal Models for Counterfactual Estimation}, 
      author={Pedro Sanchez and Sotirios A. Tsaftaris},
      year={2022},
      eprint={2202.10166},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{ribeiro2023high,
      title={High Fidelity Image Counterfactuals with Probabilistic Causal Models}, 
      author={Fabio De Sousa Ribeiro and Tian Xia and Miguel Monteiro and Nick Pawlowski and Ben Glocker},
      year={2023},
      eprint={2306.15764},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{kocaoglu2017causalgan,
  title={Causalgan: Learning causal implicit generative models with adversarial training},
  author={Kocaoglu, Murat and Snyder, Christopher and Dimakis, Alexandros G and Vishwanath, Sriram},
  journal={arXiv preprint arXiv:1709.02023},
  year={2017}
}

@misc{chao2023interventional,
      title={Interventional and Counterfactual Inference with Diffusion Models}, 
      author={Patrick Chao and Patrick Blöbaum and Shiva Prasad Kasiviswanathan},
      year={2023},
      eprint={2302.00860},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@misc{javaloy2023causal,
      title={Causal normalizing flows: from theory to practice}, 
      author={Adrián Javaloy and Pablo Sánchez-Martín and Isabel Valera},
      year={2023},
      eprint={2306.05415},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{farid2023latent,
      title={Latent Diffusion Counterfactual Explanations}, 
      author={Karim Farid and Simon Schrodi and Max Argus and Thomas Brox},
      year={2023},
      eprint={2310.06668},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}


@article{saha2022noise,
  title={On noise abduction for answering counterfactual queries: A practical outlook},
  author={Saha, Saptarshi and Garain, Utpal},
  year={2022}
}



@article{jones2023no,
  title={No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging},
  author={Jones, Charles and Castro, Daniel C and Ribeiro, Fabio De Sousa and Oktay, Ozan and McCradden, Melissa and Glocker, Ben},
  journal={arXiv preprint arXiv:2307.16526},
  year={2023}
}


@article{nasr2023counterfactual,
  title={Counterfactual (non-) identifiability of learned structural causal models},
  author={Nasr-Esfahany, Arash and Kiciman, Emre},
  journal={arXiv preprint arXiv:2301.09031},
  year={2023}
}


@article{schulte2024online,
  title={Why Online Reinforcement Learning is Causal},
  author={Schulte, Oliver and Poupart, Pascal},
  journal={arXiv preprint arXiv:2403.04221},
  year={2024}
}


@inproceedings{wu2019counterfactual,
  title={Counterfactual fairness: Unidentification, bound and algorithm},
  author={Wu, Yongkai and Zhang, Lu and Wu, Xintao},
  booktitle={Proceedings of the twenty-eighth international joint conference on Artificial Intelligence},
  year={2019}
}

@misc{baranchuk2022labelefficient,
      title={Label-Efficient Semantic Segmentation with Diffusion Models}, 
      author={Dmitry Baranchuk and Ivan Rubachev and Andrey Voynov and Valentin Khrulkov and Artem Babenko},
      year={2022},
      eprint={2112.03126},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@InProceedings{Lu_2024_WACV,
    author    = {Lu, Zeyu and Wu, Chengyue and Chen, Xinyuan and Wang, Yaohui and Bai, Lei and Qiao, Yu and Liu, Xihui},
    title     = {Hierarchical Diffusion Autoencoders and Disentangled Image Manipulation},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2024},
    pages     = {5374-5383}
}

@InProceedings{Yang_2023_ICCV,
    author    = {Yang, Xingyi and Wang, Xinchao},
    title     = {Diffusion Model as Representation Learner},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {18938-18949}
}

@misc{preechakul2022diffusion,
      title={Diffusion Autoencoders: Toward a Meaningful and Decodable Representation}, 
      author={Konpat Preechakul and Nattanat Chatthee and Suttisak Wizadwongsa and Supasorn Suwajanakorn},
      year={2022},
      eprint={2111.15640},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{li2023diffusion,
      title={Your Diffusion Model is Secretly a Zero-Shot Classifier}, 
      author={Alexander C. Li and Mihir Prabhudesai and Shivam Duggal and Ellis Brown and Deepak Pathak},
      year={2023},
      eprint={2303.16203},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}




@misc{wei2023unsupervised,
      title={Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective}, 
      author={Pengfei Wei and Lingdong Kong and Xinghua Qu and Yi Ren and Zhiqiang Xu and Jing Jiang and Xiang Yin},
      year={2023},
      eprint={2208.07365},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}


@inproceedings{nasr2023counterfactual,
  title={Counterfactual identifiability of bijective causal models},
  author={Nasr-Esfahany, Arash and Alizadeh, Mohammad and Shah, Devavrat},
  booktitle={International Conference on Machine Learning},
  pages={25733--25754},
  year={2023},
  organization={PMLR}
}


@inproceedings{graham2023denoising,
  title={Denoising diffusion models for out-of-distribution detection},
  author={Graham, Mark S and Pinaya, Walter HL and Tudosiu, Petru-Daniel and Nachev, Parashkev and Ourselin, Sebastien and Cardoso, Jorge},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2947--2956},
  year={2023}
}

@inproceedings{zhugegptswarm,
  title={GPTSwarm: Language Agents as Optimizable Graphs},
  author={Zhuge, Mingchen and Wang, Wenyi and Kirsch, Louis and Faccio, Francesco and Khizbullin, Dmitrii and Schmidhuber, J{\"u}rgen},
  booktitle={Forty-first International Conference on Machine Learning}
}


@article{lee2024general,
  title={A General Identification Algorithm For Data Fusion Problems Under Systematic Selection},
  author={Lee, Jaron JR and Ghassami, AmirEmad and Shpitser, Ilya},
  journal={arXiv preprint arXiv:2404.06602},
  year={2024}
}



@inproceedings{grover2020alignflow,
  title={Alignflow: Cycle consistent learning from multiple domains via normalizing flows},
  author={Grover, Aditya and Chute, Christopher and Shu, Rui and Cao, Zhangjie and Ermon, Stefano},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={04},
  pages={4028--4035},
  year={2020}
}


@article{frauen2023neural,
  title={A neural framework for generalized causal sensitivity analysis},
  author={Frauen, Dennis and Imrie, Fergus and Curth, Alicia and Melnychuk, Valentyn and Feuerriegel, Stefan and van der Schaar, Mihaela},
  journal={arXiv preprint arXiv:2311.16026},
  year={2023}
}


@article{chen2024causal,
  title={Causal Evaluation of Language Models},
  author={Chen, Sirui and Peng, Bo and Chen, Meiqi and Wang, Ruiqi and Xu, Mengying and Zeng, Xingyu and Zhao, Rui and Zhao, Shengjie and Qiao, Yu and Lu, Chaochao},
  journal={arXiv preprint arXiv:2405.00622},
  year={2024}
}


@misc{cho2024brief,
      title={A Brief Introduction to Causal Inference in Machine Learning}, 
      author={Kyunghyun Cho},
      year={2024},
      eprint={2405.08793},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}


@inproceedings{mao2021generative,
  title={Generative interventions for causal learning},
  author={Mao, Chengzhi and Cha, Augustine and Gupta, Amogh and Wang, Hao and Yang, Junfeng and Vondrick, Carl},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={3947--3956},
  year={2021}
}


@article{blankemeier2024merlin,
  title={Merlin: A Vision Language Foundation Model for 3D Computed Tomography},
  author={Blankemeier, Louis and Cohen, Joseph Paul and Kumar, Ashwin and Van Veen, Dave and Gardezi, Syed Jamal Safdar and Paschali, Magdalini and Chen, Zhihong and Delbrouck, Jean-Benoit and Reis, Eduardo and Truyts, Cesar and others},
  journal={arXiv preprint arXiv:2406.06512},
  year={2024}
}


@article{bachmann20244m,
  title={4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities},
  author={Bachmann, Roman and Kar, O{\u{g}}uzhan Fatih and Mizrahi, David and Garjani, Ali and Gao, Mingfei and Griffiths, David and Hu, Jiaming and Dehghan, Afshin and Zamir, Amir},
  journal={arXiv preprint arXiv:2406.09406},
  year={2024}
}

@inproceedings{mazzetto2024improved,
  title={An Improved Algorithm for Learning Drifting Discrete Distributions},
  author={Mazzetto, Alessio},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={4159--4167},
  year={2024},
  organization={PMLR}
}


@article{acharya2018learning,
  title={Learning and testing causal models with interventions},
  author={Acharya, Jayadev and Bhattacharyya, Arnab and Daskalakis, Constantinos and Kandasamy, Saravanan},
  journal={Advances in Neural Information Processing Systems},
  volume={31},
  year={2018}
}


@inproceedings{jung2024estimating,
  title={Estimating causal effects identifiable from a combination of observations and experiments},
  author={Jung, Yonghan and Moon, Jaehong and Zhang, Shuyang and Glymour, Clark},
  booktitle={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}


@article{dhawan2024end,
  title={End-to-end causal effect estimation from unstructured natural language data},
  author={Dhawan, Nikita and Cotta, Leonardo and Ullrich, Karen and Krishnan, Rahul G and Maddison, Chris J},
  journal={arXiv preprint arXiv:2407.07018},
  year={2024}
}

@article{kocaoglu2019characterization,
  title={Characterization and learning of causal graphs with latent variables from soft interventions},
  author={Kocaoglu, Murat and Jaber, Amin and Shanmugam, Karthikeyan and Bareinboim, Elias},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  year={2019}
}


@inproceedings{zhou2023causal,
  title={Causal-debias: Unifying debiasing in pretrained language models and fine-tuning via causal invariant learning},
  author={Zhou, Fan and Mao, Yuzhou and Yu, Liu and Yang, Yi and Zhong, Ting},
  booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={4227--4241},
  year={2023}
}

@article{ravfogel2024counterfactual,
  title={Counterfactual Generation from Language Models},
  author={Ravfogel, Shauli and Svete, Anej and Sn{\ae}bjarnarson, V{\'e}steinn and Cotterell, Ryan},
  journal={arXiv preprint arXiv:2411.07180},
  year={2024}
}



@ARTICLE{9766407,
  author={Cao, Xingjian and Sun, Gang and Yu, Hongfang and Guizani, Mohsen},
  journal={IEEE Internet of Things Journal}, 
  title={PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks}, 
  year={2023},
  volume={10},
  number={5},
  pages={3749-3762},
  keywords={Data models;Collaborative work;Training;Computer architecture;Task analysis;Machine learning;Computational modeling;Co-training;federated learning;generative adversarial networks (GANs);nonindependent and identically distributed (non-IID) data;personalized model},
  doi={10.1109/JIOT.2022.3172114}}



@article{hu2024fissionvae,
  title={FissionVAE: Federated Non-IID Image Generation with Latent Space and Decoder Decomposition},
  author={Hu, Chen and Deng, Jingjing and Xie, Xianghua and Ma, Xiaoke},
  journal={arXiv preprint arXiv:2408.17090},
  year={2024}
}

@article{song2021federated,
  title={Federated CycleGAN for privacy-preserving image-to-image translation},
  author={Song, Joonyoung and Ye, Jong Chul},
  journal={arXiv preprint arXiv:2106.09246},
  year={2021}
}


@inproceedings{
wang2024why,
title={Why Go Full? Elevating Federated Learning Through Partial Network Updates},
author={Haolin Wang and Xuefeng Liu and Jianwei Niu and Wenkai Guo and Shaojie Tang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=6OK8Qy9yVu}
}


@inproceedings{
wang2024flora,
title={{FL}o{RA}: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations},
author={Ziyao Wang and Zheyu Shen and Yexiao He and Guoheng Sun and Hongyi Wang and Lingjuan Lyu and Ang Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=TcCorXxNJQ}
}


@inproceedings{
makhija2024a,
title={A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings},
author={Disha Makhija and Joydeep Ghosh and Nhat Ho},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hilGwNabqB}
}


@inproceedings{ng2022towards,
  title={Towards federated bayesian network structure learning with continuous optimization},
  author={Ng, Ignavier and Zhang, Kun},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={8095--8111},
  year={2022},
  organization={PMLR}
}


@inproceedings{
tang2024fusefl,
title={Fuse{FL}: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion},
author={Zhenheng Tang and Yonggang Zhang and Peijie Dong and Yiu-ming Cheung and Amelie Chi Zhou and Bo Han and Xiaowen Chu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=E7fZOoiEKl}
}


@article{zhuang2024coala,
  title={Coala: A practical and vision-centric federated learning platform},
  author={Zhuang, Weiming and Xu, Jian and Chen, Chen and Li, Jingtao and Lyu, Lingjuan},
  journal={arXiv preprint arXiv:2407.16560},
  year={2024}
}


@article{zhang2023federated,
  title={Federated generative learning with foundation models},
  author={Zhang, Jie and Qi, Xiaohua and Zhao, Bo},
  journal={arXiv preprint arXiv:2306.16064},
  year={2023}
}

@article{bai2024federated,
  title={Federated fine-tuning of large language models under heterogeneous language tasks and client resources},
  author={Bai, Jiamu and Chen, Daoyuan and Qian, Bingchen and Yao, Liuyi and Li, Yaliang},
  journal={arXiv e-prints},
  pages={arXiv--2402},
  year={2024}
}


@article{yang2024dual,
  title={Dual-Personalizing Adapter for Federated Foundation Models},
  author={Yang, Yiyuan and Long, Guodong and Shen, Tao and Jiang, Jing and Blumenstein, Michael},
  journal={arXiv preprint arXiv:2403.19211},
  year={2024}
}

@inproceedings{jothimurugesan2023federated,
  title={Federated learning under distributed concept drift},
  author={Jothimurugesan, Ellango and Hsieh, Kevin and Wang, Jianyu and Joshi, Gauri and Gibbons, Phillip B},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={5834--5853},
  year={2023},
  organization={PMLR}
}


@article{mclaughlin2024personalized,
  title={Personalized Federated Learning via Feature Distribution Adaptation},
  author={Mclaughlin, Connor J and Su, Lili},
  journal={arXiv preprint arXiv:2411.00329},
  year={2024}
}


@inproceedings{mcmahan2017communication,
  title={Communication-efficient learning of deep networks from decentralized data},
  author={McMahan, Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and y Arcas, Blaise Aguera},
  booktitle={Artificial intelligence and statistics},
  pages={1273--1282},
  year={2017},
  organization={PMLR}
}



@book{tian2002general,
  title={A general identification condition for causal effects},
  author={Tian, Jin and Pearl, Judea},
  year={2002},
  publisher={eScholarship, University of California}
}


@inproceedings{
rahman2024modular,
title={Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference},
author={Md Musfiqur Rahman and Murat Kocaoglu},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=bOhzU7NpTB}
}


@article{cao2022perfed,
  title={PerFED-GAN: Personalized federated learning via generative adversarial networks},
  author={Cao, Xingjian and Sun, Gang and Yu, Hongfang and Guizani, Mohsen},
  journal={IEEE Internet of Things Journal},
  volume={10},
  number={5},
  pages={3749--3762},
  year={2022},
  publisher={IEEE}
}

@inproceedings{kocaoglu2018causalgan,
  title={CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training},
  author={Kocaoglu, Murat and Snyder, Christopher and Dimakis, Alexandros G and Vishwanath, Sriram},
  booktitle={International Conference on Learning Representations},
  year={2018}
}

@article{pawlowski2020deep,
  title={Deep structural causal models for tractable counterfactual inference},
  author={Pawlowski, Nick and Coelho de Castro, Daniel and Glocker, Ben},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={857--869},
  year={2020}
}


@inproceedings{zhang2021treatment,
  title={Treatment effect estimation with disentangled latent factors},
  author={Zhang, Weijia and Liu, Lin and Li, Jiuyong},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  pages={10923--10930},
  year={2021}
}


@article{mirza2014conditional,
  title={Conditional generative adversarial nets},
  author={Mirza, Mehdi and Osindero, Simon},
  journal={arXiv preprint arXiv:1411.1784},
  year={2014}
}

@article{ho2022classifier,
  title={Classifier-free diffusion guidance},
  author={Ho, Jonathan and Salimans, Tim},
  journal={arXiv preprint arXiv:2207.12598},
  year={2022}
}

@article{bishop2006pattern,
  title={Pattern recognition and machine learning},
  author={Bishop, Christopher M},
  journal={Springer google schola},
  volume={2},
  pages={645--678},
  year={2006}
}


@article{han2021federated,
  title={Federated adaptive causal estimation (face) of target treatment effects},
  author={Han, Larry and Hou, Jue and Cho, Kelly and Duan, Rui and Cai, Tianxi},
  journal={arXiv preprint arXiv:2112.09313},
  year={2021}
}


@article{xiong2023federated,
  title={Federated causal inference in heterogeneous observational data},
  author={Xiong, Ruoxuan and Koenecke, Allison and Powell, Michael and Shen, Zhu and Vogelstein, Joshua T and Athey, Susan},
  journal={Statistics in Medicine},
  volume={42},
  number={24},
  pages={4418--4439},
  year={2023},
  publisher={Wiley Online Library}
}


@article{qiao2023collaborative,
  title={Collaborative causal inference with fair incentives},
  author={Qiao, Rui and Xu, Xinyi and Low, Bryan Kian Hsiang},
  year={2023}
}


@inproceedings{bareinboim2015recovering,
  title={Recovering causal effects from selection bias},
  author={Bareinboim, Elias and Tian, Jin},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={29},
  number={1},
  year={2015}
}

@article{tikka2019causal,
  title={Causal effect identification from multiple incomplete data sources: A general search-based approach},
  author={Tikka, Santtu and Hyttinen, Antti and Karvanen, Juha},
  journal={arXiv preprint arXiv:1902.01073},
  year={2019}
}


@inproceedings{tang2023learning,
  title={Learning personalized causally invariant representations for heterogeneous federated clients},
  author={Tang, Xueyang and Guo, Song and Zhang, Jie and Guo, Jingcai},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2023}
}

@inproceedings{tangcausally,
  title={Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization},
  author={Tang, Xueyang and Guo, Song and Guo, Jingcai and Zhang, Jie and Yu, Yue},
  booktitle={Forty-first International Conference on Machine Learning}
}


@article{francis2021towards,
  title={Towards causal federated learning for enhanced robustness and privacy},
  author={Francis, Sreya and Tenison, Irene and Rish, Irina},
  journal={arXiv preprint arXiv:2104.06557},
  year={2021}
}

@article{rahman2024conditional,
  title={Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand},
  author={Rahman, Md Musfiqur and Jordan, Matt and Kocaoglu, Murat},
  journal={arXiv preprint arXiv:2402.07419},
  year={2024}
}

@inproceedings{jiang2023approximate,
  title={Approximate causal effect identification under weak confounding},
  author={Jiang, Ziwei and Wei, Lai and Kocaoglu, Murat},
  booktitle={International Conference on Machine Learning},
  pages={15125--15143},
  year={2023},
  organization={PMLR}
}

@inproceedings{jiangconditional,
  title={Conditional Common Entropy for Instrumental Variable Testing and Partial Identification},
  author={Jiang, Ziwei and Kocaoglu, Murat},
  booktitle={Forty-first International Conference on Machine Learning}
}


@inproceedings{yang2025feature,
  title={Feature Diversification and Adaptation for Federated Domain Generalization},
  author={Yang, Seunghan and Choi, Seokeon and Park, Hyunsin and Choi, Sungha and Chang, Simyung and Yun, Sungrack},
  booktitle={European Conference on Computer Vision},
  pages={52--70},
  year={2025},
  organization={Springer}
}

@article{huang2021importance,
  title={On the importance of gradients for detecting distributional shifts in the wild},
  author={Huang, Rui and Geng, Andrew and Li, Yixuan},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={677--689},
  year={2021}
}

@article{li2024federated,
  title={Federated Causal Discovery from Heterogeneous Data},
  author={Li, Loka and Ng, Ignavier and Luo, Gongxu and Huang, Biwei and Chen, Guangyi and Liu, Tongliang and Gu, Bin and Zhang, Kun},
  journal={arXiv preprint arXiv:2402.13241},
  year={2024}
}


@article{ikram2022root,
  title={Root cause analysis of failures in microservices through causal discovery},
  author={Ikram, Azam and Chakraborty, Sarthak and Mitra, Subrata and Saini, Shiv and Bagchi, Saurabh and Kocaoglu, Murat},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={31158--31170},
  year={2022}
}


@inproceedings{huang2023rethinking,
  title={Rethinking federated learning with domain shift: A prototype view},
  author={Huang, Wenke and Ye, Mang and Shi, Zekun and Li, He and Du, Bo},
  booktitle={2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={16312--16322},
  year={2023},
  organization={IEEE}
}


@article{singh2023augmenting,
  title={Augmenting interpretable models with large language models during training},
  author={Singh, Chandan and Askari, Armin and Caruana, Rich and Gao, Jianfeng},
  journal={Nature Communications},
  volume={14},
  number={1},
  pages={7913},
  year={2023},
  publisher={Nature Publishing Group UK London}
}


@inproceedings{
wang2024taming,
title={Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains},
author={Lei Wang and Jieming Bian and Letian Zhang and Chen Chen and Jie Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=6SRPizFuaE}
}

@article{chen2024,
  title={Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views},
  author={Chen, Xinyue and Ren, Yazhou and Xu, Jie and Lin, Fangfei and Pu, Xiaorong and Yang, Yang},
  journal={arXiv preprint arXiv:2410.09484},
  year={2024}
}


@inproceedings{
chen2024confusionresistant,
title={Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-{IID} Data},
author={xiaohong chen and Canran Xiao and Yongmei liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=G89r8Mgi5r}
}

@inproceedings{
chen2024classifier,
title={Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift},
author={Junbao Chen and Jingfeng Xue and Yong Wang and Zhenyan Liu and Lu Huang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=6ejpSVIiIl}
}


@article{wangpersonalized,
  title={Personalized Federated Learning with Spurious Features: An Adversarial Approach},
  author={Wang, Xiaoyang and Zhao, Han and Nahrstedt, Klara and Koyejo, Sanmi},
  journal={Transactions on Machine Learning Research},
 year={2024}
}



@article{ma2024reducing,
  title={Reducing Spurious Correlation for Federated Domain Generalization},
  author={Ma, Shuran and Xie, Weiying and Li, Daixun and Li, Haowei and Li, Yunsong},
  journal={arXiv preprint arXiv:2407.19174},
  year={2024}
}


@inproceedings{yu2023turning,
  title={Turning the curse of heterogeneity in federated learning into a blessing for out-of-distribution detection},
  author={Yu, Shuyang and Hong, Junyuan and Wang, Haotao and Wang, Zhangyang and Zhou, Jiayu},
  booktitle={2023 International Conference on Learning Representations},
  year={2023}
}


@inproceedings{
liao2024foogd,
title={{FOOGD}: Federated Collaboration for Both Out-of-distribution Generalization and Detection},
author={Xinting Liao and Weiming Liu and Pengyang Zhou and Fengyuan Yu and Jiahe Xu and Jun Wang and Wenjie Wang and Chaochao Chen and Xiaolin Zheng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=D6MQrw9HFu}
}


@article{tan2024heterogeneity,
  title={Is heterogeneity notorious? taming heterogeneity to handle test-time shift in federated learning},
  author={Tan, Yue and Chen, Chen and Zhuang, Weiming and Dong, Xin and Lyu, Lingjuan and Long, Guodong},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}




@article{shpitser2008complete,
  title={Complete identification methods for the causal hierarchy},
  author={Shpitser, Ilya and Pearl, Judea},
  year={2008}
}

@inproceedings{liu2015faceattributes,
  title = {Deep Learning Face Attributes in the Wild},
  author = {Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
  booktitle = {Proceedings of International Conference on Computer Vision (ICCV)},
  month = {December},
  year = {2015} 
}

@article{arjovsky2019invariant,
  title={Invariant Risk Minimization},
  author={Arjovsky, Martin and Bottou, L{\'e}on and Gulrajani, Ishaan and Lopez-Paz, David},
  journal={arXiv preprint arXiv:1907.02893},
  year={2019}
}


@article{chen2024modeling,
  title={Modeling Latent Selection with Structural Causal Models},
  author={Chen, Leihao and Zoeter, Onno and Mooij, Joris M},
  journal={arXiv preprint arXiv:2401.06925},
  year={2024}
}


@inproceedings{huang2020causal,
  title={Causal discovery from multiple data sets with non-identical variable sets},
  author={Huang, Biwei and Zhang, Kun and Gong, Mingming and Glymour, Clark},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={34},
  number={06},
  pages={10153--10161},
  year={2020}
}



@article{shpitser2012identification,
  title={Identification of conditional interventional distributions},
  author={Shpitser, Ilya and Pearl, Judea},
  journal={arXiv preprint arXiv:1206.6876},
  year={2012}
}

@article{bareinboim2012causal,
  title={Causal inference by surrogate experiments: z-identifiability},
  author={Bareinboim, Elias and Pearl, Judea},
  journal={arXiv preprint arXiv:1210.4842},
  year={2012}
}

@inproceedings{lee2020general,
  title={General identifiability with arbitrary surrogate experiments},
  author={Lee, Sanghack and Correa, Juan D and Bareinboim, Elias},
  booktitle={Uncertainty in artificial intelligence},
  pages={389--398},
  year={2020},
  organization={PMLR}
}

@article{bareinboim2013general,
  title={A general algorithm for deciding transportability of experimental results},
  author={Bareinboim, Elias and Pearl, Judea},
  journal={Journal of causal Inference},
  volume={1},
  number={1},
  pages={107--134},
  year={2013},
  publisher={De Gruyter}
}


@inproceedings{bareinboim2012controlling,
  title={Controlling selection bias in causal inference},
  author={Bareinboim, Elias and Pearl, Judea},
  booktitle={Artificial Intelligence and Statistics},
  pages={100--108},
  year={2012},
  organization={PMLR}
}



@article{jerzak2022image,
  title={Image-based treatment effect heterogeneity},
  author={Jerzak, Connor T and Johansson, Fredrik and Daoud, Adel},
  journal={arXiv preprint arXiv:2206.06417},
  year={2022}
}

@article{qin2021causal,
  title={Causal interventional training for image recognition},
  author={Qin, Wei and Zhang, Hanwang and Hong, Richang and Lim, Ee-Peng and Sun, Qianru},
  journal={IEEE Transactions on Multimedia},
  year={2021},
  publisher={IEEE}
}


@inproceedings{shalit2017estimating,
  title={Estimating individual treatment effect: generalization bounds and algorithms},
  author={Shalit, Uri and Johansson, Fredrik D and Sontag, David},
  booktitle={International conference on machine learning},
  pages={3076--3085},
  year={2017},
  organization={PMLR}
}

@article{louizos2017causal,
  title={Causal effect inference with deep latent-variable models},
  author={Louizos, Christos and Shalit, Uri and Mooij, Joris M and Sontag, David and Zemel, Richard and Welling, Max},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@article{wu2019pc,
  title={Pc-fairness: A unified framework for measuring causality-based fairness},
  author={Wu, Yongkai and Zhang, Lu and Wu, Xintao and Tong, Hanghang},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}


@inproceedings{xu2019achieving,
  title={Achieving causal fairness through generative adversarial networks},
  author={Xu, Depeng and Wu, Yongkai and Yuan, Shuhan and Zhang, Lu and Wu, Xintao},
  booktitle={Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence},
  year={2019}
}

@inproceedings{parafita2021deep,
  title={Deep Causal Graphs for Causal Inference, Black-Box Explainability and Fairness},
  author={PARAFITA, Alvaro and VITRIA, Jordi},
  booktitle={Artificial Intelligence Research and Development: Proceedings of the 23rd International Conference of the Catalan Association for Artificial Intelligence},
  volume={339},
  pages={415},
  year={2021},
  organization={IOS Press}
}


@inproceedings{subbaswamy2019preventing,
  title={Preventing failures due to dataset shift: Learning predictive models that transport},
  author={Subbaswamy, Adarsh and Schulam, Peter and Saria, Suchi},
  booktitle={The 22nd International Conference on Artificial Intelligence and Statistics},
  pages={3118--3127},
  year={2019},
  organization={PMLR}
}

@inproceedings{lee2023finding,
  title={Finding invariant predictors efficiently via causal structure},
  author={Lee, Kenneth and Rahman, Md Musfiqur and Kocaoglu, Murat},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={1196--1206},
  year={2023},
  organization={PMLR}
}

@article{bao2024adaptive,
  title={Adaptive test-time personalization for federated learning},
  author={Bao, Wenxuan and Wei, Tianxin and Wang, Haohan and He, Jingrui},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}

@article{xiong2021federated,
  title={Federated causal inference in heterogeneous observational data},
  author={Xiong, Ruoxuan and Koenecke, Allison and Powell, Michael and Shen, Zhu and Vogelstein, Joshua T and Athey, Susan},
  journal={arXiv preprint arXiv:2107.11732},
  year={2021}
}


@article{ye2024federated,
  title={Federated learning of generalized linear causal networks},
  author={Ye, Qiaoling and Amini, Arash A and Zhou, Qing},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2024},
  publisher={IEEE}
}


@inproceedings{
khellaf2025federated,
title={Federated Causal Inference: Multi-Centric {ATE} Estimation beyond Meta-Analysis},
author={R{\'e}mi Khellaf and Aur{\'e}lien Bellet and Julie Josse},
booktitle={The 28th International Conference on Artificial Intelligence and Statistics},
year={2025},
url={https://openreview.net/forum?id=1QovyEffl5}
}

@inproceedings{hwang2024positivity,
  title={On positivity condition for causal inference},
  author={Hwang, Inwoo and Choe, Yesong and Kwon, Yeahoon and Lee, Sanghack},
  booktitle={Forty-first International Conference on Machine Learning},
  year={2024}
}


@article{markoulidakis2021balance,
  title={How balance and sample size impact bias in the estimation of causal treatment effects: a simulation study},
  author={Markoulidakis, Andreas and Holmans, Peter and Pallmann, Philip and Busse, Monica and Griffin, Beth Ann},
  journal={arXiv preprint arXiv:2107.09009},
  year={2021}
}


@incollection{pearl1995bayesian,
  title={From Bayesian networks to causal networks},
  author={Pearl, Judea},
  booktitle={Mathematical models for handling partial knowledge in artificial intelligence},
  pages={157--182},
  year={1995},
  publisher={Springer}
}



@article{sharma2020dowhy,
  title={DoWhy: An end-to-end library for causal inference},
  author={Sharma, Amit and Kiciman, Emre},
  journal={arXiv preprint arXiv:2011.04216},
  year={2020}
}

@article{balazadeh2022partial,
  title={Partial identification of treatment effects with implicit generative models},
  author={Balazadeh Meresht, Vahid and Syrgkanis, Vasilis and Krishnan, Rahul G},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={22816--22829},
  year={2022}
}


@article{gao2021federated,
  title={Federated causal discovery},
  author={Gao, Erdun and Chen, Junjia and Shen, Li and Liu, Tongliang and Gong, Mingming and Bondell, Howard},
  year={2021}
}



@inproceedings{gresele2022causal,
  title={Causal inference through the structural causal marginal problem},
  author={Gresele, Luigi and Von K{\"u}gelgen, Julius and K{\"u}bler, Jonas and Kirschbaum, Elke and Sch{\"o}lkopf, Bernhard and Janzing, Dominik},
  booktitle={International conference on machine learning},
  pages={7793--7824},
  year={2022},
  organization={PMLR}
}