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Facts
| Fact | rguo12/awesome-causality-algorithms An index of algorithms for learning causality with data | py-why/EconML ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x. |
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| Stars | 3,302 | 4,851 |
| Net stars, last 30 days | +11 | +46 |
| Largest star day | +2 on Sep 16, 2026 | +9 on Jun 29, 2026 |
| Milestone reached in the record | None | None |
| Forks | 477 | 834 |
| Language | Not reported | Jupyter Notebook |
| License | MIT | Other license |
| Created | Sep 19, 2018 | Apr 30, 2018 |
| Last push | Oct 28, 2026 | Nov 12, 2026 |
| Archived | No | No |
| Health facts: Maintenance | 2 of 4 checks (2 without data) | 2 of 4 checks (2 without data) |
| Health facts: Releases | 0 of 3 checks (2 without data) | 2 of 3 checks (1 without data) |
| Health facts: Security practice | 0 of 1 check (1 without data) | 0 of 1 check (1 without data) |
| Health facts: Community files | 1 of 3 checks (2 without data) | 1 of 3 checks (2 without data) |
| Commits, last 13 weeks | No data | No data |
| Weeks with commits, last 52 | No data | No data |
| In the Repository Index since | Nov 11, 2026 | Nov 10, 2026 |
Topics that all of them have on the Repository Host: causality, causal-inference. The facts come from the Repository Host and from the Star History that AwesomeRepos records. A shared topic does not mean that one repository can replace another. Each Health Facts row is one dimension alone. AwesomeRepos does not add them into one score. How each check is defined