---
title: "py-why/dowhy: papers citing this GitHub repository"
description: "4 papers in the tracked journals link to the GitHub repository py-why/dowhy in their Crossref records, the first in 2020; most of them (2) in 2021."
url: https://scholar4ai.com/code/github-py-why-dowhy
markdown_url: https://scholar4ai.com/code/github-py-why-dowhy.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 py-why/dowhy in their Crossref records, the first in 2020; most of them (2) in 2021. (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)
---

# py-why/dowhy: papers linking to it

Research papers in the 480 tracked journals that link to py-why/dowhy (https://github.com/py-why/dowhy), 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
- 8,385 stars
- 1,065 forks
- Python
- license MIT
- created 2018
- https://github.com/py-why/dowhy

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

## Key findings

- 4 papers in the tracked journals link to the GitHub repository py-why/dowhy in their Crossref records, the first in 2020; most of them (2) in 2021.
- By category: AI (3) and Finance (1); the journals with the most are Digital Finance (1), Applied Intelligence (1) and Information Processing & Management (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links py-why/dowhy.

## By year

*Papers linking to py-why/dowhy by publication year*

| Year | Papers |
|---|---:|
| 2020 | 1 |
| 2021 | 2 |
| 2025 | 1 |

## Journals

*Journals whose papers link to py-why/dowhy most*

| Journal | Papers |
|---|---:|
| [Digital Finance](https://scholar4ai.com/journals/digital-finance.md) | 1 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |
| [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | 1 |
| [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Emotional Intensity-based Success Prediction Model for Crowdfunded Campaigns](https://doi.org/10.1016/j.ipm.2020.102394) (Information Processing & Management, 2020)
  - [DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing](https://doi.org/10.1007/s12559-021-09825-w) (Cognitive Computation, 2021) — cites the paper above

## Papers

*Papers linking to py-why/dowhy, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2021-06-01 | [CATE meets ML](https://doi.org/10.1007/s42521-021-00033-7) | [Digital Finance](https://scholar4ai.com/journals/digital-finance.md) | Daniel Jacob | 46 | cites it |
| 2020-09-28 | [Emotional Intensity-based Success Prediction Model for Crowdfunded Campaigns](https://doi.org/10.1016/j.ipm.2020.102394) | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | Stefano Faralli, Steve Rittinghaus, Nima Samsami et al. | 21 | cites it |
| 2021-02-04 | [DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing](https://doi.org/10.1007/s12559-021-09825-w) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Damiano Distante, Stefano Faralli, Steve Rittinghaus et al. | 6 | cites it |
| 2025-01-07 | [Enhanced causal effects estimation based on offline reinforcement learning](https://doi.org/10.1007/s10489-024-06009-5) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Huan Xia, Chaozhe Jiang, Chenyang Zhang | 2 | 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). py-why/dowhy: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-py-why-dowhy

```bibtex
@misc{scholar4ai-code-github-py-why-dowhy,
  author = {{Scholar4AI}},
  title = {py-why/dowhy: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-py-why-dowhy}},
  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/).