dsldPropens: dsldMatchedATE

In dsld: Data Science Looks at Discrimination

dsldMatchedATE

Description

Causal inference via matching models. Wrapper for Matching::Match.

Usage

dsldMatchedATE(data,yName,sName,yesSVal,yesYVal=NULL,
   propensFtn=NULL,k=NULL)

Arguments

data	Data frame.
yName	Name of the response variable column.
sName	Name of the sensitive attribute column. The attribute must be dichotomous.
yesSVal	S value to be considered "yes," to be coded 1 rather than 0.
yesYVal	Y value to be considered "yes," to be coded 1 rather than 0.
propensFtn	Either 'glm' (logistic), or 'knn'.
k	Number of nearest neighbors if propensFtn='knn'.

Details

This is a dsld wrapper for Matching::Match.

Matched analysis is typically applied to measuring "treatment effects," but is often applied in situations in which the "treatment," S here, is an immutable attribute such as race or gender. The usual issues concerning observational studies apply.

The function dsldMatchedATE finds the estimated mean difference between the matched Y pairs in the treated/nontreated (exposed and non-exposed) groups, with covariates X in data other than the yName and sName columns.

In the propensity model case, we estimate P(S = 1 | X), either by a logistic or k-NN model.

Value

Object of class 'Match'. See documentation in the Matching package.

Author(s)

N. Matloff

Examples

data(lalonde,package='Matching')
ll <- lalonde
ll$treat <- as.factor(ll$treat)
ll$re74 <- NULL
ll$re75 <- NULL
summary(dsldMatchedATE(ll,'re78','treat','1')) 
summary(dsldMatchedATE(ll,'re78','treat','1',propensFtn='glm'))
summary(dsldMatchedATE(ll,'re78','treat','1',propensFtn='knn',k=15))

dsld documentation built on Sept. 14, 2024, 1:08 a.m.