ConditionalEffect: Estimating the Conditional Effects with the CausalANOVA.
In FindIt: Finding Heterogeneous Treatment Effects

Description Usage Arguments Details Value Author(s) References See Also Examples

ConditionalEffect estimates a variety of conditional effects using the ouput from CausalANOVA.

ConditionalEffect(
  object,
  treat.fac = NULL,
  cond.fac = NULL,
  base.ind = 1,
  round = 3,
  inference = NULL,
  verbose = TRUE
)

`object`	The output from `CausalANOAV` function.
`treat.fac`	The name of factor acting as the main treatment variable.
`cond.fac`	The name of factor acting as the conditioning (moderating) variable.
`base.ind`	An indicator for the baseline of the treatment factor. Default is 1.
`round`	Digits to round estimates. Default is 3.
`inference`	(optional). This argument is mainly for internal use. It indicates whether `CausalANOVA` has done inference or not.
`verbose`	Whether it prints the progress.

See Details in CausalANOVA.

`CondtionalEffects`	The summary of estimated conditional effects.
`...`	Arguments for the internal use.

Naoki Egami and Kosuke Imai.

Egami, Naoki and Kosuke Imai. 2019. Causal Interaction in Factorial Experiments: Application to Conjoint Analysis, Journal of the American Statistical Association. http://imai.fas.harvard.edu/research/files/int.pdf

Lim, M. and Hastie, T. 2015. Learning interactions via hierarchical group-lasso regularization. Journal of Computational and Graphical Statistics 24, 3, 627–654.

Post, J. B. and Bondell, H. D. 2013. “Factor selection and structural identification in the interaction anova model.” Biometrics 69, 1, 70–79.

CausalANOVA.

data(Carlson)
## Specify the order of each factor
Carlson$newRecordF<- factor(Carlson$newRecordF,ordered=TRUE,
                            levels=c("YesLC", "YesDis","YesMP",
                                     "noLC","noDis","noMP","noBusi"))
Carlson$promise <- factor(Carlson$promise,ordered=TRUE,levels=c("jobs","clinic","education"))
Carlson$coeth_voting <- factor(Carlson$coeth_voting,ordered=FALSE,levels=c("0","1"))
Carlson$relevantdegree <- factor(Carlson$relevantdegree,ordered=FALSE,levels=c("0","1"))

## ####################################### 
## Without Screening and Collapsing
## ####################################### 
#################### AMEs and two-way AMIEs ####################
fit2 <- CausalANOVA(formula=won ~ newRecordF + promise + coeth_voting + relevantdegree,
                    int2.formula = ~ newRecordF:coeth_voting,
                    data=Carlson, pair.id=Carlson$contestresp,diff=TRUE,
                    cluster=Carlson$respcodeS, nway=2)
summary(fit2)
plot(fit2, type="ConditionalEffect", fac.name=c("newRecordF","coeth_voting"))
ConditionalEffect(fit2, treat.fac="newRecordF", cond.fac="coeth_voting")