mlr.smd

Calculate standardized mean difference for each column of a matrix, given a binary treatment indicator vector.

Core functions as well as diagnostic and calibration tools for combining matching and linear regression for causal inference in observational studies.

Alireza Mahani

MatchLinReg

Combining Matching and Linear Regression for Causal Inference

mlr.smd function

<dl> <dt>tr</dt>
<dd>Binary treatment indicator vector; 1 means treatment, 0 means control.</dd> <dt>X</dt>
<dd>Matrix of covariates; each column is a covariate whose standardized mean difference we want to calculate. <code>nrow(X)</code> must be equal to <code>length(tr)</code>.</dd></dl>

Arguments

Alireza S. Mahani, Mansour T.A. Sharabiani

Author

Standardized mean difference — mlr.smd

<dl>

 <dt>tr</dt>
<dd>Binary treatment indicator vector; 1 means treatment, 0 means control.</dd>

 <dt>X</dt>
<dd>Matrix of covariates; each column is a covariate whose standardized mean difference we want to calculate. <code>nrow(X)</code> must be equal to <code>length(tr)</code>.</dd>

</dl>

mlr.smd: Standardized mean difference

Description

Usage

Value

Arguments

Author