estimate_Sid_bound: Estimate the partial identification bound as in Siddique...
In ivitr: Estimate IV-Optimal Individualized Treatment Rules

Description Usage Arguments Value Examples

View source: R/estimate_Sid_bound.R

estimate_Sid_bound estimates the partial identification bound for each instance in the input dataset with a binary IV, observed covariates, a binary treatment indicator, and a binary outcome according to Siddique (2013, JASA).

1	estimate_Sid_bound(dt, method = "rf", nodesize = 5)

`dt`	A dataframe whose first column is a binary IV 'Z', followed by q columns of observed covariates, followed by a binary treatment indicator 'A', and finally followed by a binary outcome 'Y'. The dataset has q+3 columns in total.
`method`	A character string indicator the method used to estimate each constituent conditional probability of the partial identification bound. Users can choose to fit multinomial regression by setting method = 'multinom', and random forest by setting method = 'rf'.
`nodesize`	Node size to be used in a random forest algorithm if method is set to 'rf'. The default value is set to 5.

The original dataframe with two additional columns: L and U. L indicates the lower bound and U the upper bound as in Siddique 2013

attach(dt_Rouse)
# Construct an IV out of differential distance to two-year versus
# four-year college. Z = 1 if the subject lives not farther from
# a 4-year college compared to a 2-year college.
Z = (dist4yr <= dist2yr) + 0

# Treatment A = 1 if the subject attends a 4-year college and 0
# otherwise.
A = 1 - twoyr

# Outcome Y = 1 if the subject obtained a bachelor's degree
Y = (educ86 >= 16) + 0

# Prepare the dataset
dt = data.frame(Z, female, black, hispanic, bytest, dadsome,
     dadcoll, momsome, momcoll, fincome, fincmiss, A, Y)

# Calculate the Siddique bound by estimating each constituent
# conditional probability p(Y = y, A = a | Z, X) with a random
# forest.
dt_with_Sid_bound_rf = estimate_Sid_bound(dt, method = 'rf', nodesize = 5)

# Calculate the Siddique bound by estimating each constituent
# conditional probability p(Y = y, A = a | Z, X) with a multinomial
# regression.
dt_with_Sid_bound_multinom = estimate_Sid_bound(dt, method = 'multinom')