Ordinal Causal Discovery
Overview
Algorithms for ordinal causal discovery. This package aims to enable users to discover causality for observational ordinal categorical data with greedy and exhaustive search. See Ni, Y., & Mallick, B. (2022) https://proceedings.mlr.press/v180/ni22a/ni22a.pdf "Ordinal Causal Discovery. Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, (UAI 2022), PMLR 180:1530–1540".
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 1
- Examples that run
- 100%1 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Repository
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- Median days to close, 50 most recently updated closed issues
- 1
- Median days to close, 50 most recently updated closed PRs
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Stars over time
Forks over time
Issues over time
2 development-tooling and community-health practices detected across 2 families in the upstream repository
Checks run against github.com/nystat/ordcd on 2026-10-22.
Dependencies
Code & Tests
People & History
Package metadata
- First published
- 2022-09-27
- Total releases
- 4 / 4 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 7.9 KB
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