crossclassified_data: Sample cross-classified data for analysis

In youmisuk/CURobustML: Robust Machine Learning Under Cluster-Level Unmeasured Confounding

Sample cross-classified data for analysis

Description

Sample cross-classified data where individuals (e.g., students) belong to two clusters simultaneously (e.g., schools and neighborhoods)

Usage

crossclassified_data

Format

Sample data contains:

f1id

factor-1 cluster (e.g., school) identifier

f1id

factor-2 cluster (e.g., neighborhood) identifier

f12id

combined factor-12 cluster (e.g., interaction of school and neighborhood) identifier

Y

continuous outcome

Z

binary treatment (1=treated, 0=control)

X1

first individual-level confounder

X2

second individual-level confounder

X3

third individual-level confounder

W1

first factor-1 confounder (e.g., school climate)

W2

second factor-1 confounder, considered as omitted/measured

Q1

first factor-2 confounder (e.g., perceived neighborhood safety)

Q2

second factor-2 confounder, considered as omitted/measured

lps

true propensity score logit

ps

true propensity score

Y1

true potential treatment outcome

Y0

true potential control outcome

youmisuk/CURobustML documentation built on Sept. 11, 2022, 11:04 a.m.