MLCausal
0.1.0Causal Inference Methods for Multilevel and Clustered Data
Overview
Provides an end-to-end workflow for estimating average treatment effects in clustered (multilevel) observational data. Core functionality includes cluster-aware propensity score estimation using fixed effects and Mundlak-style specifications, inverse probability weighting, within-cluster nearest-neighbor matching, covariate balance diagnostics at both individual and cluster-mean levels, outcome regression with cluster-robust standard errors, propensity score overlap visualization, and tipping-point sensitivity analysis for omitted cluster-level confounding.
Install
Health
- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-04-163 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 97%
- Return-value docs
- 100%
- References docs
- 13%
Downloads
Repository
Repository practices
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Checks run against github.com/causalfragility-lab/mlcausal on 2026-07-31.
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.0Latest2026-04-15 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-04-15
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.1.0
- Download size
- not tracked yet
- Installed size
- not tracked yet
- With dependencies
- not tracked yet