py-why/dowhy: papers linking to it

GitHub · 8,385 stars · 1,065 forks · Python · license MIT · created 2018 · github.com/py-why/dowhy

← Code and data repositories cited in research papers

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.

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#

20202025

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	1
Applied Intelligence	1
Information Processing & Management	1
Cognitive Computation	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

Emotional Intensity-based Success Prediction Model for Crowdfunded Campaigns Information Processing & Management, 2020
- DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing Cognitive Computation, 2021 — cites the paper above

Papers linking to py-why/dowhy, most cited first
Published	Title	Journal	Authors	Citations	Link
2021-06-01	CATE meets ML	Digital Finance	Daniel Jacob	46	cites it
2020-09-28	Emotional Intensity-based Success Prediction Model for Crowdfunded Campaigns	Information Processing & Management	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	Cognitive Computation	Damiano Distante, Stefano Faralli, Steve Rittinghaus et al.	6	cites it
2025-01-07	Enhanced causal effects estimation based on offline reinforcement learning	Applied Intelligence	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

@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}
}

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