perm_test

point estimates of the percentile distance between P(X|I=0) &amp; P(X|I=1).

estimates

wgt1

numeric data frame or matrix of factors to be considered.

number of quantiles to use when calculating CDF distance.

n.quant

number of permutations to permute from wgt1.

n.perm

outcome vector (must be numeric without NA's).

numeric data frame or matrix of factors to control for. these are factors
that we can't consider while looking for the optimal intervention
(e.g. race).

control

either "non_parm" or "nn". for "nn", weights are recalculated for each
permutation, and thus Y and control are needed. the default is "non_parm",
and Y and control aren't needed.

func

Permutation test
Test the null hypothesis P(X|I=0) = P(X|I=1), using permutation test.


Provides both parametric and non-parametric estimates of the
correlates of some desired
outcome (e.g. test scores, income) using a new method proposed by
Danieli, Devi and Fryer (2019). This method relaxes the assumption
that one can alter individual characteristics in any way the data
suggest is optimal, and so can be used anytime one wants to use observational
data to better optimize social experiments designed to increase some desired
outcome.

perm_test: Permutation test Test the null hypothesis P(X|I=0) = P(X|I=1), using permutation test.

Description

Usage

Arguments

Value