# Research papers

Here you can find a (non-exhaustive) 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="2026-05-07" 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="2025-09-27" tags="chamber-data,sbi" %}

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

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

[*arXiv preprint arXiv:2509.23385*](https://arxiv.org/abs/2509.23385)
{% 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 %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.causalchamber.ai/case-studies/in-the-literature/research-papers.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.