[cem] Problems with CEM

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