boot_ATE: boot_ATE: Bootstrapped marginal effects
In kmcconeghy/Scotty: Scotty: misc. functions

Description Usage Arguments Details Value Author(s)

Estimate Average Treatment Effect with Bootstrapped CI's

1	boot_ATE(model, treat, R = 250, block = "", df)

`model`	GLM regression object
`treat`	Character string for variable to calculate ATE with
`R`	Integer for number of bootstrap replications
`block`	Character string for "block" to sample by (cluster robust SE's).
`df`	Dataframe object of original sample

boot_ATE will take a glm regression object and compute predicted outcomes with treatment = 1 and treatment 0; It will then bootstrap the model and compute effects. There is an option for block bootstrapping to compute cluster robust intervals. Bootstrapped confidence intervals are percentile.

coefficient from glm object

coefficient from bootstraps

ATE (p1- p0)

RR (p1/p0)

matrix of bootstrap replications

Kevin W. McConeghy

kmcconeghy/Scotty documentation built on Aug. 26, 2020, 11:45 p.m.