py-why/EconML: papers linking to it

GitHub · 4,839 stars · 834 forks · Jupyter Notebook · created 2018 · github.com/py-why/EconML

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Research papers in the 480 tracked journals that link to py-why/EconML (https://github.com/py-why/EconML), 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#

8 papers in the tracked journals link to the GitHub repository py-why/EconML in their Crossref records, the first in 2021; most of them (4) in 2026.
By category: AI (4), Finance (1) and Economics (1); the journals with the most are Machine Learning (3), Digital Finance (1) and Journal of Econometrics (1).
2 of the papers build on one another: a later paper cites an earlier one that also links py-why/EconML.

By year#

20212026

Papers linking to py-why/EconML by publication year
Year	Papers
2021	1
2024	1
2025	2
2026	4

Journals#

Journals whose papers link to py-why/EconML most
Journal	Papers
Machine Learning	3
Digital Finance	1
Journal of Econometrics	1
Health Care Management Science	1
Future Business Journal	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.

Meta-learning for heterogeneous treatment effect estimation with closed-form solvers Machine Learning, 2024
- Federated causal inference from observational data Machine Learning, 2025 — cites the paper above

Papers linking to py-why/EconML, most cited first
Published	Title	Journal	Authors	Citations	Link
2021-06-01	CATE meets ML	Digital Finance	Daniel Jacob	46	cites it
2025-07-22	Federated causal inference from observational data	Machine Learning	Thanh Vinh Vo, Young Lee, Tze-Yun Leong	3	cites it
2024-05-29	Meta-learning for heterogeneous treatment effect estimation with closed-form solvers	Machine Learning	Tomoharu Iwata, Yoichi Chikahara	1	cites it
2025-11-25	Robust treatment assignment with uncertainty-aware causal forests: Joint optimization of accuracy and estimation uncertainty	Expert Systems with Applications	Yufan Ji, Abdollah Shafieezadeh	1	cites it
2026-02-19	Healthcare applications of 0-1 neural networks in prescriptive problems with observational data	Health Care Management Science	Vrishabh Patil, Kara Kjersten Hoppe, Yonatan Dov Mintz	1	cites it
2026-10-16	The Challenges of Hyperparameter Tuning for Accurate Causal Effect Estimation	Machine Learning	Damian Machlanski, Spyridon Samothrakis, Paul Clarke	1	cites it
2026-02-28	Statistical inference of optimal allocations I: Regularities and their implications	Journal of Econometrics	Kai Feng, Han Hong, Denis Nekipelov	0	cites it
2026-03-04	Speculation and retail price transmission in the frozen concentrated orange juice market: a causal machine learning analysis	Future Business Journal	Alessio Abeltino, Andrea Pannone, Andrea Bernardini	0	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/EconML: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-py-why-econml

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

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