> 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

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="2026-07-13" tags="chamber-data,causal-inference" %}

## 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" %}

## 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="2026-06-16" 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" %}

## Towards Continuous-time Causal Foundation Models

Dennis Thumm, Ruben Wiedemann, Ying Chen

[*arXiv preprint arXiv:2605.28880*](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="2026-03-10" tags="chamber-data,causal-inference" %}

## Nonparametric Greedy Equivalence Search with Prior-Fitted Networks

Mateusz Gajewski, Mateusz Olko

*Proceedings of Machine Learning Research, Vol. 323, pp. 1–26*
{% 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

[*arXiv preprint arXiv:2602.17187*](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

[*arXiv preprint arXiv:2601.10531*](https://arxiv.org/abs/2601.10531)
{% 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="2025-10-23" 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

[*arXiv preprint arXiv:2508.09904*](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 %}