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MLCausal  

Causal Inference Methods for Multilevel and Clustered Data
View on CRAN: Click here


Download and install MLCausal package within the R console
Install from CRAN:
install.packages("MLCausal")

Install from Github:
library("remotes")
install_github("cran/MLCausal")

Install by package version:
library("remotes")
install_version("MLCausal", "0.1.0")



Attach the package and use:
library("MLCausal")
Maintained by
Subir Hait
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-04-15
Latest Update: 2026-04-15
Description:
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.
How to cite:
Subir Hait (2026). MLCausal: Causal Inference Methods for Multilevel and Clustered Data. R package version 0.1.0, https://cran.r-project.org/web/packages/MLCausal. Accessed 12 Sep. 2026.
Previous versions and publish date:
No previous versions
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Complete documentation for MLCausal
Functions, R codes and Examples using the MLCausal R package
Full MLCausal package functions and examples
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