> For the complete documentation index, see [llms.txt](https://docs.causalchamber.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.causalchamber.ai/case-studies/in-the-literature/research-papers.md).

# Research papers

How the scientific community uses the Chambers and their data.

Here you can find a selection of research **papers that use the Chambers as a testbed** to validate algorithms and methodology, either with data collected through the [Remote Lab](/remote-lab/quickstart.md), or from datasets in our open-source [repository](https://github.com/juangamella/causal-chamber).

For the complete list of papers citing the chambers, including those doing so as motivation or related work, please check the [Google Scholar](https://scholar.google.com/scholar?oi=bibs\&hl=en\&cites=10210437611267445381,8869775343600774328) page.

{% hint style="info" %}
Did we miss your paper? Has the preprint been published?

Let us know at <contact@causalchamber.ai> and we'll be happy to fix it!
{% endhint %}

### By publication date

***

{% updates format="full" %}
{% update date="2026-09-15" tags="chamber-data,causal-inference,foundation-models" %}

## LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence

Xingxuan Zhang, Gang Ren, Hao Yuan, Hao Zou, Hongze Tan, Hui Wang, Jianhao Song, Jiansheng Li, Jiayao Zhang, Jinghan Zhang, Kaifang Li, Lang Mo, Li Mao, Mingchao Hao, Nuo Xu, Rui Ding, Ruiji Zhang, Shuyang Li, Siyu Mei, Tianyang Zhang, Weiyang Mu, Yancheng Dong, Yongxian Wei, Yuan Xue, Yuanrui Wang, Yue He, Zijia Yang, Ziyun Li, Dongzhe Li, Fuqiang Wang, Jiandong Liu, Jiawei Chen, Jiaxin Du, Kaijie Cheng, Kehan Li, Lei Sun, Linjun Zhou, Ningbo Dai, Qi Wang, Renzhe Xu, Shaoxing Du, Shumeng Yang, Wang Lu, Wenjing Chu, Xiannan Huang, Xiaoyu Lin, Xing Ai, Xinyan Han, Xuanyue Li, Xuanyue Su, Xukun Zhang, Yan Lu, Yaxin Zhang, Yi Qin, Yifei Huang, Yihan Xu, Yongle Lv, Yuanyuan Jiang, Yushan Han, Peng Cui

[*arXiv preprint arXiv:2609.17488*](https://arxiv.org/abs/2609.17488)
{% endupdate %}

{% update date="2026-09-10" tags="chamber-data,causal-inference,foundation-models" %}

## CausalArena: Benchmarking Causal Discovery in the Foundation Model Era

Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang, Han-Jia Ye

[*arXiv preprint arXiv:2609.11897*](https://arxiv.org/abs/2609.11897)
{% endupdate %}

{% update date="2026-09-01" tags="chamber-data,causal-inference" %}

## Optimization Methods for Sparse Statistical Learning: Exact Formulations, Scalable Algorithms, and Coordinate-Optimality Reformulations

Tong Xu

[Doctoral Dissertation, Northwestern University](https://www.proquest.com/openview/1d618f9b36744a033181cc45e7cdcd42/1)
{% endupdate %}

{% update date="2026-08-28" tags="chamber-data,causal-inference" %}

## I-FLOP: Fast Learning of Order and Parents from Interventional Data

Liuting Chen, Alex Markham

[*arXiv preprint arXiv:2608.28245*](https://arxiv.org/abs/2608.28245)
{% endupdate %}

{% update date="2026-08-28" tags="chamber-data,llms,agents" %}

## Fidelity Is Not Enough: Dispatch-Level Instrumentation for Agentic Datasheet Extraction

Qing Ye, Meng-Hsuan Lin

[*arXiv preprint arXiv:2608.28439*](https://arxiv.org/abs/2608.28439)
{% endupdate %}

{% update date="2026-08-15" tags="chamber-data,causal-inference" %}

## GFCM: A Tail-Sensitive Mixed-Type Conditional Independence Test for Causal Discovery

Pavel Averin, Theodoros Moysiadis, Ioannis Katakis

[*arXiv preprint arXiv:2608.15332*](https://arxiv.org/abs/2608.15332)
{% endupdate %}

{% update date="2026-08-10" tags="chamber-data,experiment-design" %}
Testing when adaptive data acquisition can replace\
fixed measurement plans
-----------------------

Jia Bi, Samuel Pinilla, Chenyang Zhu

[*arXiv preprint arXiv:2607.27651*](https://arxiv.org/abs/2607.27651)
{% endupdate %}

{% update date="2026-07-13" tags="chamber-data,causal-inference,foundation-models" %}

## DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms

Yikang Chen, Zhengkang Guan, Haoyuan Qian, Peng Cui, Yi Yang, Kun Kuang

[*arXiv preprint arXiv:2607.11510*](https://arxiv.org/abs/2607.11510)
{% endupdate %}

{% update date="2026-07-13" tags="chamber-data,causal-inference,foundation-models" %}

## CDFM: Towards a General-Purpose Causal Discovery Foundation Model

Jie Qiao, Ruichu Cai, Zijian Li, Weilin Chen, Pengfei Hua, Boyan Xu, Zhengming Chen, Zhifeng Hao, Peng Cui

[*arXiv preprint arXiv:2607.11508*](https://arxiv.org/abs/2607.11508)
{% endupdate %}

{% update date="2026-07-13" tags="chamber-data,causal-inference,uai" %}

## Falsifying Causal Graphs With Outlier Events

William Roy Orchard, Philipp M. Faller, Dominik Janzing

[Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence, PMLR 337:5081-5110, 2026.](https://proceedings.mlr.press/v337/orchard26a.html)
{% endupdate %}

{% update date="2026-07-13" tags="chamber-data,causal-inference,icml" %}

## Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?

Felix Schur, Niklas Pfister, Peng Ding, Sach Mukherjee, Jonas Peters

[ICML 2026](https://openreview.net/pdf?id=gqa99Ev4C4)
{% endupdate %}

{% update date="2026-07-02" tags="chamber-data,anomaly-detection,predictive-maintenance" %}

## Online and Federated Learning for Predictive Maintenance in Heavy-Duty Vehicles

Kartikey Sharma

[Master Thesis at SCANIA x Uppsala Universitet](https://uu.diva-portal.org/smash/get/diva2:2083501/FULLTEXT01.pdf)
{% endupdate %}

{% update date="2026-06-16" tags="chamber-data,causal-inference" %}

## FoundCause: Causal Discovery with Latent Confounders from Observational Data

Patrick Blöbaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan

[*arXiv preprint arXiv:2606.17516*](https://arxiv.org/abs/2606.17516)
{% endupdate %}

{% update date="2026-06-16" tags="chamber-data,causal-inference" %}

## Tensor-based second-order causal discovery

Nathan Ouyang, Kexin Wang, Anna Seigal

[*arXiv preprint arXiv:2606.18074*](https://arxiv.org/abs/2606.18074)
{% endupdate %}

{% update date="2026-06-10" tags="chamber-data,causal-inference,domain-generalization" %}

## How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?

Julia Kostin, Kasra Jalaldoust, Elias Bareinboim, Samory Kpotufe, Fanny Yang

[*arXiv preprint arXiv:2606.12680*](https://arxiv.org/abs/2606.12680)
{% endupdate %}

{% update date="2026-06-04" tags="chamber-data,causal-inference" %}

## EML-CD: Causal Mechanism Recovery via EML Symbolic Trees in Structure Learning

Sota Asanuma

[*arXiv preprint arXiv:2606.05942*](https://arxiv.org/abs/2606.05942)
{% endupdate %}

{% update date="2026-06-03" tags="chamber-data,sbi,icml" %}

## Flow Matching Calibration for Simulation-Based Inference under Model Misspecification

Pierre-Louis Ruhlmann, Michael Arbel, Florence Forbes, Pedro L. C. Rodrigues

[*ICML 2026*](https://arxiv.org/abs/2509.23385)
{% endupdate %}

{% update date="2026-06-03" tags="chamber-data,agents,llms" %}

## Harnessing Generalist Agents for Contextualized Time Series

Zihao Li, Kaifeng Jin, Yuanchen Bei, Jiaru Zou, Avaneesh Kumar, Xuying Ning, Yanjun Zhao, Mengting Ai, Baoyu Jing, Hanghang Tong, Jingrui He

[*arXiv preprint arXiv:2606.05404*](https://arxiv.org/abs/2606.05404)
{% endupdate %}

{% update date="2026-05-26" tags="causal-inference,chamber-data,root-causal-analysis" %}

## ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis

Phi Nguyen Xuan, Nicholas Tagliapietra, Lavdim Halilaj, Kristian Kersting, Juergen Luettin

[*arXiv preprint arXiv:2605.27022*](https://arxiv.org/abs/2605.27022)
{% endupdate %}

{% update date="2026-05-26" tags="causal-inference,chamber-data,foundation-models,icml" %}

## Towards Continuous-time Causal Foundation Models

Dennis Thumm, Ruben Wiedemann, Ying Chen

[*ICML 2026 Workshop on Foundation Models for Structured Data (FMSD)*](https://arxiv.org/abs/2605.28880)
{% endupdate %}

{% update date="2026-05-16" tags="chamber-data,causal-inference" %}

## Prediction-Intervention Games and Invariant Sets

Linus Kühne, Felix Schur, Jonas Peters

[*arXiv preprint arXiv:2605.16828*](https://arxiv.org/abs/2605.16828)
{% endupdate %}

{% update date="2026-05-13" tags="chamber-data,reinforcement-learning,robotics,icml" %}

## Trajectory-Level Data Augmentation for Offline Reinforcement Learning

Tobias Schmähling, Matthias Burkhardt, Tobias Windisch

[*To appear in ICML*](https://arxiv.org/abs/2605.13401)
{% endupdate %}

{% update date="2026-05-07" tags="chamber-data,sbi" %}

## Information-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference

Joon Jang, Eunho Jeong, Kyu Sung Choi, Hyeonjin Kim

[*arXiv preprint arXiv:2605.05652*](https://arxiv.org/abs/2605.05652)
{% endupdate %}

{% update date="2026-04-10" tags="chamber-data,causal-inference" %}

## Identifying Causal Effects Using a Single Proxy Variable

Silvan Vollmer, Niklas Pfister, Sebastian Weichwald

[*arXiv preprint arXiv:2604.09135*](https://arxiv.org/abs/2604.09135)

Read the [case study.](/case-studies/causal-inference/effect-estimation-with-proxies.md)
{% endupdate %}

{% update date="2026-04-10" tags="chamber-data,causal-inference" %}

## Causal generalized linear models via Pearson risk invariance

Alice Polinelli, Veronica Vinciotti, Ernst C. Wit

[*Journal of Causal Inference, Vol. 14, No. 1, Art. 20240043 (De Gruyter)*](https://arxiv.org/abs/2407.16786)
{% endupdate %}

{% update date="2026-03-18" tags="chamber-data,causal-inference,uai" %}

## How PC-based Methods Err: Towards Better Reporting of Assumption Violations and Small Sample Errors

Sofia Faltenbacher, Jonas Wahl, Rebecca Herman, Jakob Runge

[*Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence*, PMLR 337:1498-1519, 2026.](https://proceedings.mlr.press/v337/faltenbacher26a.html)
{% endupdate %}

{% update date="2026-03-10" tags="chamber-data,causal-inference" %}

## Nonparametric Greedy Equivalence Search with Prior-Fitted Networks

Mateusz Gajewski, Mateusz Olko

[*Proceedings of the Fifth Conference on Causal Learning and Reasoning*, PMLR 323:1171-1197](https://proceedings.mlr.press/v323/gajewski26a.html)
{% endupdate %}

{% update date="2026-02-20" tags="chamber-data,causal-inference,domain-generalization,icml" %}

## Anti-causal domain generalization: Leveraging unlabeled data

Sorawit Saengkyongam, Juan L. Gamella, Andrew C. Miller, Jonas Peters, Nicolai Meinshausen, Christina Heinze-Deml

[*Proceedings of the 42nd International Conference on Machine Learning (ICML 2026), PMLR 306*](https://arxiv.org/abs/2602.17187)
{% endupdate %}

{% update date="2026-02-06" tags="chamber-data,hybrid-models" %}

## Learning Deep Hybrid Models with Sharpness-Aware Minimization

Naoya Takeishi

[*arXiv preprint arXiv:2602.06837*](https://arxiv.org/abs/2602.06837)
{% endupdate %}

{% update date="2026-01-30" tags="chamber-data,causal-inference,anomaly-detection" %}

## Causal Characterization of Measurement and Mechanistic Anomalies

Hendrik Suhr, David Kaltenpoth, Jilles Vreeken

[*arXiv preprint arXiv:2601.23026*](https://arxiv.org/abs/2601.23026)
{% endupdate %}

{% update date="2026-01-15" tags="chamber-data,causal-inference" %}

## Coarsening Causal DAG Models

Francisco Madaleno, Pratik Misra, Alex Markham

[*CLeaR 2026, PMLR 323, pp. 1318–1344*](https://proceedings.mlr.press/v323/madaleno26b.html)
{% endupdate %}

{% update date="2025-12-05" tags="chamber-data,sbi,neurips" %}

## Inductive Domain Transfer In Misspecified Simulation-Based Inference

Ortal Senouf, Antoine Wehenkel, Cédric Vincent-Cuaz, Emmanuel Abbé, Pascal Frossard

[*Advances in Neural Information Processing Systems 38 (NeurIPS 2025)*](https://neurips.cc/virtual/2025/loc/san-diego/poster/118196)
{% endupdate %}

{% update date="2025-11-26" tags="chamber-data,causal-inference" %}

## Convex Mixed-Integer Programming for Causal Additive Models with Optimization and Statistical Guarantees

Xiaozhu Zhang, Nir Keret, Ali Shojaie, Armeen Taeb

[*arXiv preprint arXiv:2511.21126*](https://arxiv.org/abs/2511.21126)
{% endupdate %}

{% update date="2025-11-13" tags="chamber-data,causal-inference" %}

## Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning

Yihong Gu, Cong Fang, Peter Bühlmann, Jianqing Fan

[*The Annals of Statistics, Vol. 53, No. 5, pp. 2230–2257*](https://people.math.ethz.ch/~buhlmann/publications/AOS2541.pdf)
{% endupdate %}

{% update date="2025-11-03" tags="chamber-data,causal-inference" %}

## Causal Regularization: On the trade-off between in-sample risk and out-of-sample risk guarantees

Lucas Kania, Ernst Wit

[*arXiv preprint arXiv:2205.01593*](https://arxiv.org/abs/2205.01593)
{% endupdate %}

{% update date="2025-10-23" tags="chamber-data,causal-inference,neurips" %}

## Flow-Based Non-stationary Temporal Regime Causal Structure Learning

Abdellah Rahmani, Pascal Frossard

[*Advances in Neural Information Processing Systems 38 (NeurIPS 2025)*](https://arxiv.org/abs/2506.17065)
{% endupdate %}

{% update date="2025-10-01" tags="chamber-data,density-estimation" %}

## CINDES: Classification induced neural density estimator and simulator

Dehao Dai, Jianqing Fan, Yihong Gu, Debarghya Mukherjee

[*arXiv preprint arXiv:2510.00367*](https://arxiv.org/abs/2510.00367)
{% endupdate %}

{% update date="2025-08-13" tags="chamber-data,llms" %}

## Beyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs

Arjun Ashok, Andrew Robert Williams, Vincent Zhihao Zheng, Irina Rish, Nicolas Chapados, Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin

[*Transactions on Machine Learning Research (TMLR), 2026*](https://arxiv.org/abs/2508.09904)
{% endupdate %}

{% update date="2025-07-21" tags="chamber-data,causal-inference,bayesian-optimization" %}

## Towards MFACBO: Multi-Fidelity Abstraction Causal Bayesian Optimization in the Context of the Abstraction-Fidelity Connection

Jakob Zeitler

[*1st Workshop on Causal Abstractions and Representations (CAR), UAI 2025*](https://openreview.net/forum?id=elj9C1sqp4)
{% endupdate %}

{% update date="2025-07-13" tags="chamber-data,llm-benchmark,icml" %}

## Context is Key: A Benchmark for Forecasting with Essential Textual Information

Andrew Robert Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin

[*Proceedings of the 42nd International Conference on Machine Learning (ICML 2025), PMLR 267, pp. 66887–66944*](https://proceedings.mlr.press/v267/williams25a.html)
{% endupdate %}

{% update date="2025-07-13" tags="chamber-data,sbi,icml" %}

## Addressing Misspecification in Simulation-based Inference through Data-driven Calibration

Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Jörn-Henrik Jacobsen, Marco Cuturi

[*Proceedings of the 42nd International Conference on Machine Learning (ICML 2025), PMLR 267 (oral presentation)*](https://icml.cc/virtual/2025/oral/47170)
{% endupdate %}

{% update date="2025-07-13" tags="chamber-data,causal-inference,icml" %}

## Sanity Checking Causal Representation Learning on a Simple Real-World System

Juan L. Gamella, Simon Bing, Jakob Runge

[*Proceedings of the 42nd International Conference on Machine Learning (ICML 2025) (oral presentation)*](https://icml.cc/virtual/2025/oral/47207)
{% endupdate %}

{% update date="2025-06-22" tags="chamber-data,domain-generalization,jmlr" %}

## Invariant Subspace Decomposition

Margherita Lazzaretto, Jonas Peters, Niklas Pfister

[*Journal of Machine Learning*](https://www.jmlr.org/papers/v26/24-0699.html)​[ *Research, Vol. 26, No. 95, pp. 1–56*](https://www.jmlr.org/papers/v26/24-0699.html)
{% endupdate %}

{% update date="2025-05-01" tags="chamber-data,causal-inference" %}

## Algorithmic Statistical Learning and Causality Pursuit Using Neural Networks

Yihong Gu

[*PhD Thesis, Princeton University (Department of Operations Research and Financial Engineering)*](https://dataspace.princeton.edu/handle/88435/dsp01np193d590)
{% endupdate %}

{% update date="2025-03-12" tags="chamber-data,causal-inference" %}

## Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions

Juan L. Gamella, Armeen Taeb, Christina Heinze-Deml, Peter Bühlmann

[*arXiv preprint arXiv:2211.14897*](https://arxiv.org/abs/2211.14897)
{% endupdate %}
{% endupdates %}