---
title: "vdorie/npci: papers citing this GitHub repository"
description: "4 papers in the tracked journals link to the GitHub repository vdorie/npci in their Crossref records, the first in 2021; most of them (1) in 2026."
url: https://scholar4ai.com/code/github-vdorie-npci
markdown_url: https://scholar4ai.com/code/github-vdorie-npci.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "4 papers in the tracked journals link to the GitHub repository vdorie/npci in their Crossref records, the first in 2021; most of them (1) in 2026. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# vdorie/npci: papers linking to it

Research papers in the 480 tracked journals that link to vdorie/npci (https://github.com/vdorie/npci), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- GitHub
- 52 stars
- 16 forks
- R
- created 2015
- https://github.com/vdorie/npci

All repositories: https://scholar4ai.com/code.md

## Key findings

- 4 papers in the tracked journals link to the GitHub repository vdorie/npci in their Crossref records, the first in 2021; most of them (1) in 2026.
- By category: AI (4); the journals with the most are Neural Computing and Applications (2), Machine Learning (1) and Data Mining and Knowledge Discovery (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links vdorie/npci.

## By year

*Papers linking to vdorie/npci by publication year*

| Year | Papers |
|---|---:|
| 2021 | 1 |
| 2024 | 1 |
| 2025 | 1 |
| 2026 | 1 |

## Journals

*Journals whose papers link to vdorie/npci most*

| Journal | Papers |
|---|---:|
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 2 |
| [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 1 |
| [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Mutual information-based neighbor selection method for causal effect estimation](https://doi.org/10.1007/s00521-024-09555-8) (Neural Computing and Applications, 2024)
  - [C-XGBoost model with targeted regularization for treatment effect estimation](https://doi.org/10.1007/s00521-026-12114-y) (Neural Computing and Applications, 2026) — cites the paper above

## Papers

*Papers linking to vdorie/npci, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2021-05-17 | [Adversarial balancing-based representation learning for causal effect inference with observational data](https://doi.org/10.1007/s10618-021-00759-3) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Xin Du, Lei Sun, Wouter Duivesteijn et al. | 26 | cites it |
| 2024-02-23 | [Mutual information-based neighbor selection method for causal effect estimation](https://doi.org/10.1007/s00521-024-09555-8) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Niki Kiriakidou, Ioannis E. Livieris, Panagiotis Pintelas | 16 | cites it |
| 2025-07-22 | [Federated causal inference from observational data](https://doi.org/10.1007/s10994-025-06819-9) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Thanh Vinh Vo, Young Lee, Tze-Yun Leong | 3 | cites it |
| 2026-05-28 | [C-XGBoost model with targeted regularization for treatment effect estimation](https://doi.org/10.1007/s00521-026-12114-y) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Niki Kiriakidou, Ioannis E. Livieris, Christos Diou" | 1 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). vdorie/npci: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-vdorie-npci

```bibtex
@misc{scholar4ai-code-github-vdorie-npci,
  author = {{Scholar4AI}},
  title = {vdorie/npci: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-vdorie-npci}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).