---
title: "py-why/EconML: papers citing this GitHub repository"
description: "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."
url: https://scholar4ai.com/code/github-py-why-econml
markdown_url: https://scholar4ai.com/code/github-py-why-econml.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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. (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/EconML: papers linking to it

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.

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

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

## 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

*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](https://scholar4ai.com/journals/machine-learning.md) | 3 |
| [Digital Finance](https://scholar4ai.com/journals/digital-finance.md) | 1 |
| [Journal of Econometrics](https://scholar4ai.com/journals/journal-of-econometrics.md) | 1 |
| [Health Care Management Science](https://scholar4ai.com/journals/health-care-management-science.md) | 1 |
| [Future Business Journal](https://scholar4ai.com/journals/future-business-journal.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Meta-learning for heterogeneous treatment effect estimation with closed-form solvers](https://doi.org/10.1007/s10994-024-06546-7) (Machine Learning, 2024)
  - [Federated causal inference from observational data](https://doi.org/10.1007/s10994-025-06819-9) (Machine Learning, 2025) — cites the paper above

## Papers

*Papers linking to py-why/EconML, 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 |
| 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 |
| 2024-05-29 | [Meta-learning for heterogeneous treatment effect estimation with closed-form solvers](https://doi.org/10.1007/s10994-024-06546-7) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 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](https://doi.org/10.1016/j.eswa.2025.130578) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Yufan Ji, Abdollah Shafieezadeh | 1 | cites it |
| 2026-02-19 | [Healthcare applications of 0-1 neural networks in prescriptive problems with observational data](https://doi.org/10.1007/s10729-025-09751-5) | [Health Care Management Science](https://scholar4ai.com/journals/health-care-management-science.md) | Vrishabh Patil, Kara Kjersten Hoppe, Yonatan Dov Mintz | 1 | cites it |
| 2026-10-16 | [The Challenges of Hyperparameter Tuning for Accurate Causal Effect Estimation](https://doi.org/10.1007/s10994-026-07140-9) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Damian Machlanski, Spyridon Samothrakis, Paul Clarke | 1 | cites it |
| 2026-02-28 | [Statistical inference of optimal allocations I: Regularities and their implications](https://doi.org/10.1016/j.jeconom.2026.106217) | [Journal of Econometrics](https://scholar4ai.com/journals/journal-of-econometrics.md) | 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](https://doi.org/10.1186/s43093-026-00777-x) | [Future Business Journal](https://scholar4ai.com/journals/future-business-journal.md) | 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

```bibtex
@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}
}
```

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