To Whom It May Concern,
I'm having a problem performing the causal inference portion of CEM with my
data. Right now I have data with no missing values that categorizes citizen
responses to political questions on voting behavior (which party bloc voted
for, measured on an ordinal discrete scale), and political attitudes (two
barometer questions, also measured on an ordinal discrete scale), as well as
a number of background covariates (age, education, political interest,
knowledge, religion, gender, etc.). My treatment variable is a 1 or 0
depending on whether a respondent comes from an ethnically diverse district
or not .
In running my CEM model, everything compiled just fine, however when trying
to estimate the SATT, I get the following error:
Error in att(mat1, as.factor(ONEPAR) ~ ELF + MUSLIM + CHRISTIAN + AGE + :
please, only use `lm' or `glm'
I suppose I'm getting this because I chose a forest model for my estimation,
but I don't see why this should be a problem. Just for reference here is my
matching code and my att code.
AGECUT <- c(0,29.5, 39.5, 49.5, 59.5, 69.5, 79.5)
mat1 <- cem(treatment="TREAT1", data=data2, drop=c("RESPNO", "BACKCHK",
"URBRUR", "PROVINCE", "DISTRICT", "ELF", "DIVISION", "LOCATION", "DISCUSS",
"AUTH", "PATR", "ONEPAR","MULTPAR", "FREEVOTE", "FREEEXP", "SINGLEAD",
"ELECT", "REPRESENT", "SALIENCE", "TRUST1", "TRUST2", "TRUST3", "REFORM1",
"REFORM2", "Q88C_KEN", "VOTE", "RATIONALE", "PATRON", "FRVOTE", "FREEXP",
"ELEC", "MULTIPAR", "REFORM", "OTHERREL", "TREAT", "TREATMED2"),
cutpoints=list(AGE=AGECUT))
mat1
est1 <- att(mat1,
ONEPAR~ELF+MUSLIM+CHRISTIAN+AGE+KNOW+INTEREST+EDUC+ETHMAJ+MALE, data=data2,
model="forest")
est1
If you have any idea of what I'm doing incorrectly I'd appreciate the help.
My model runs fine using automated CEM via MatchIt, so I'm not sure what the
problem is here. Finally, I'm getting some weird results with MatchIt --
namely, almost all the covariates on which I matched are turning up as
significant. Any ideas as to what could be motivating this troubling
anomaly? Note it is my understanding (possibly incorrect) that if treatment
and control units are matched exactly, there should be no difference between
treatment and control on matched covariates, and thus these measures should
not significantly affect the outcome in the analysis.
Sincerely,
Ashley Anderson
--
Ph.D. Candidate
Harvard University Government Dept
--
Ph.D. Candidate
Harvard University Government Dept
