Causal Inference Methods for Multilevel and Clustered Data R package MLCausal version 0.1.0 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. Software Depends: R (>= 4.1.0) Imports: stats, sandwich (>= 3.0-0), lmtest (>= 0.9-38), ggplot2 (>= 3.3.0), rlang (>= 0.4.0) Suggests: testthat (>= 3.0.0), knitr (>= 1.36), rmarkdown (>= 2.11) Subir Hait <haitsubi@msu.edu> Comprehensive R Archive Network (CRAN) Subir Hait [aut, cre] (ORCID: <https://orcid.org/0009-0004-9871-9677>) MIT + file LICENSE (https://CRAN.R-project.org/package=MLCausal/LICENSE) 2026-04-15 application/tgz https://CRAN.R-project.org/package=MLCausal doi:10.32614/CRAN.package.MLCausal