| Title: | Estimate IV-Optimal Individualized Treatment Rules |
|---|---|
| Description: | A method that estimates an IV-optimal individualized treatment rule. An individualized treatment rule is said to be IV-optimal if it minimizes the maximum risk with respect to the putative IV and the set of IV identification assumptions. Please refer to <arXiv:2002.02579> for more details on the methodology and some theory underpinning the method. Function IV-PILE() uses functions in the package 'locClass'. Package 'locClass' can be accessed and installed from the 'R-Forge' repository via the following link: <https://r-forge.r-project.org/projects/locclass/>. Alternatively, one can install the package by entering the following in R: 'install.packages("locClass", repos="<http://R-Forge.R-project.org>")'. |
| Authors: | Bo Zhang |
| Maintainer: | Bo Zhang <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2024-11-26 06:25:50 UTC |
| Source: | CRAN |
Variables of the dataset is as follows:
Years of education since 1986.
Attending a two-year college immediately after high school.
Gender: 1 if female and 0 otherwise.
Race: 1 if African American and 0 otherwise.
Race: 1 if Hispanic and 0 otherwise.
Test score.
Dad's education: some college.
Dad's education: college.
Mom's education: some college.
Mom's education: college.
Family income.
Missingness indicator for family income.
Average state two-year college tuition.
Average state four-year college tuition.
Distance to the nearest two-year college.
Distance to the nearest four-year college.
data(dt_Rouse)data(dt_Rouse)
A data frame with 4437 rows and 16 columns.
ss
estimate_BP_bound estimates the Balke-Pearl bound for
each instance in the input dataset with a binary IV, observed
covariates, a binary treatment indicator, and a binary outcome.
estimate_BP_bound(dt, method = "rf", nodesize = 5)estimate_BP_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 Balke-Pearl 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 Balke-Pearl lower bound and U is the Balke-Pearl upper bound.
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 Balke-Pearl bound by estimating each constituent # conditional probability p(Y = y, A = a | Z, X) with a random # forest. dt_with_BP_bound_rf = estimate_BP_bound(dt, method = 'rf', nodesize = 5) # Calculate the Balke-Pearl bound by estimating each constituent # conditional probability p(Y = y, A = a | Z, X) with a multinomial # regression. dt_with_BP_bound_multinom = estimate_BP_bound(dt, method = 'multinom')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 Balke-Pearl bound by estimating each constituent # conditional probability p(Y = y, A = a | Z, X) with a random # forest. dt_with_BP_bound_rf = estimate_BP_bound(dt, method = 'rf', nodesize = 5) # Calculate the Balke-Pearl bound by estimating each constituent # conditional probability p(Y = y, A = a | Z, X) with a multinomial # regression. dt_with_BP_bound_multinom = estimate_BP_bound(dt, method = 'multinom')
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).
estimate_Sid_bound(dt, method = "rf", nodesize = 5)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')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')
IV_PILE estimates an IV-optimal individualized treatment
rule given a dataset with estimated partial identification intervals
for each instance.
IV_PILE(dt, kernel = "linear", C = 1, sig = 1/(ncol(dt) - 5))IV_PILE(dt, kernel = "linear", C = 1, sig = 1/(ncol(dt) - 5))
dt |
A dataframe whose first column is a binary IV 'Z', followed by q columns of observed covariates, a binary treatment indicator 'A', a binary outcome 'Y', lower endpoint of the partial identification interval 'L', and upper endpoint of the partial identification interval 'U'. The dataset has q+5 columns in total. |
kernel |
The kernel used in the weighted SVM algorithm. The user may choose between 'linear' (linear kernel) and 'radial' (Gaussian RBF kernel). |
C |
Cost of violating the constraint. This is the parameter C in the Lagrange formulation. |
sig |
Sigma in the Gaussian RBF kernel. Default is set to 1/dimension of covariates, i.e., 1/q. This parameter is not relevant for linear kernel. |
An object of the type wsvm, inheriting from svm.
## Not run: # It is necessary to install the package locClass in order # to run the following code. 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) # Estimate the Balke-Pearl bound by estimating each constituent # conditional probability p(Y = y, A = a | Z, X) with a multinomial # regression. dt_with_BP_bound_multinom = estimate_BP_bound(dt, method = 'multinom') # Estimate the IV-optimal individualized treatment rule using a # linear kernel, under the putative IV and the Balke-Pearl bound. iv_itr_BP_linear = IV_PILE(dt_with_BP_bound_multinom, kernel = 'linear') ## End(Not run)## Not run: # It is necessary to install the package locClass in order # to run the following code. 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) # Estimate the Balke-Pearl bound by estimating each constituent # conditional probability p(Y = y, A = a | Z, X) with a multinomial # regression. dt_with_BP_bound_multinom = estimate_BP_bound(dt, method = 'multinom') # Estimate the IV-optimal individualized treatment rule using a # linear kernel, under the putative IV and the Balke-Pearl bound. iv_itr_BP_linear = IV_PILE(dt_with_BP_bound_multinom, kernel = 'linear') ## End(Not run)