MLCausal
Causal Inference Methods for Multilevel and Clustered Data
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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | MLCausal_0.1.0.tar.gz |
94.4 KiB |
0.1.0 |
rolling linux/noble R-4.5 | MLCausal_0.1.0.tar.gz |
94.5 KiB |
0.1.0 |
rolling source/ R- | MLCausal_0.1.0.tar.gz |
39.1 KiB |
0.1.0 |
latest linux/jammy R-4.5 | MLCausal_0.1.0.tar.gz |
94.4 KiB |
0.1.0 |
latest linux/noble R-4.5 | MLCausal_0.1.0.tar.gz |
94.5 KiB |
0.1.0 |
latest source/ R- | MLCausal_0.1.0.tar.gz |
39.1 KiB |
0.1.0 |
2026-04-26 source/ R- | MLCausal_0.1.0.tar.gz |
39.1 KiB |
0.1.0 |
2026-04-23 source/ R- | MLCausal_0.1.0.tar.gz |
39.1 KiB |