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hettx (version 0.1.3)

Fisherian and Neymanian Methods for Detecting and Measuring Treatment Effect Variation

Description

Implements methods developed by Ding, Feller, and Miratrix (2016) , and Ding, Feller, and Miratrix (2018) for testing whether there is unexplained variation in treatment effects across observations, and for characterizing the extent of the explained and unexplained variation in treatment effects. The package includes wrapper functions implementing the proposed methods, as well as helper functions for analyzing and visualizing the results of the test.

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Version

Install

install.packages('hettx')

Monthly Downloads

250

Version

0.1.3

License

GPL (>= 3)

Maintainer

Ben Fifield

Last Published

August 19th, 2023

Functions in hettx (0.1.3)

WSKS.t

WSKS.t
get.p.value

get p-value along with uncertainty on p-value
plot.RI.R2.result

Make a plot of the treatment effect R2 estimates
rq.stat

rq.stat
estimate_systematic

Calculate systematic effects model using LATE, ITT, or full potential outcomes.
detect_idiosyncratic

detect_idiosyncratic
hettx-package

Fisherian and Neymanian Methods for Detecting and Measuring Treatment Effect Variation
variance.ratio.test

Variance ratio test
make.randomized.compliance.dat

Generate fake data with noncompliance.
test.stat.info

test.stat.info
coef.RI.regression.result

Extract coefficients of a fit RI regression model.
make.randomized.dat

Make fake data for simulations
plot.FRTCI.test

plot.FRTCI.test
vcov.RI.regression.result

Get vcov() from object.
SKS.pool.t

SKS.pool.t
R2

Estimate treatment variation R2
ToyData

Toy data set
SKS.stat.int.cov.pool

SKS.stat.int.cov.pool
SKS.stat

SKS.stat
SE

Extract the standard errors from a var-cov matrix.
KS.stat

KS.stat
Penn46_ascii

Sample data set
SKS.stat.cov.pool

SKS.stat.cov.pool
make.linear.data

Generate dataset according to a linear model.
SKS.stat.cov.rq

SKS.stat.cov.rq