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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "MLCausal" in publications use:'
type: software
license: MIT
title: 'MLCausal: Causal Inference Methods for Multilevel and Clustered Data'
version: 0.1.0
identifiers:
- type: doi
  value: 10.32614/CRAN.package.MLCausal
abstract: 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.
authors:
- family-names: Hait
  given-names: Subir
  email: haitsubi@msu.edu
  orcid: https://orcid.org/0009-0004-9871-9677
preferred-citation:
  type: manual
  title: 'MLCausal: Causal Inference Methods for Multilevel and Clustered Data'
  authors:
  - family-names: Hait
    given-names: Subir
    email: haitsubi@msu.edu
    orcid: https://orcid.org/0009-0004-9871-9677
  year: '2026'
  notes: R package version 0.1.0
  url: https://github.com/causalfragility-lab/MLCausal
repository: https://causalfragility-lab.r-universe.dev
repository-code: https://github.com/causalfragility-lab/MLCausal
commit: a0632ea1ed2525926f6a35d0512cc34f4c5577d5
url: https://github.com/causalfragility-lab/MLCausal
date-released: '2026-04-07'
contact:
- family-names: Hait
  given-names: Subir
  email: haitsubi@msu.edu
  orcid: https://orcid.org/0009-0004-9871-9677