causalreg (version 0.1.2)

cglm: Causal generalized linear model

Description

This function does a search for a causal submodel within the generalized linear model provided.

Usage

cglm(
  formula,
  family,
  data,
  alpha = 0.05,
  pval = c("bootstrap", "chi-square"),
  B = 100,
  search = c("all", "stepwise"),
  ...
)

Value

A glm object of the selected causal submodel.

Arguments

formula

A formula object.

family

A distributional family object. Currently supported options are: binomial and poisson.

data

A data frame containing the variables in the model.

alpha

Significance level for statistical test

pval

If pval="bootstrap", a bootstrap test is conducted to test whether Pearson risk is 1. When family="poisson" a chi-squared test can be conducted by setting pval="chi-square".

B

Number of bootstrap samples when pval="bootstrap". Default is 100.

search

If search="stepwise", a greedy forward stepwise search is conducted. Default is search="all", in which case all possible submodels are considered.

...

Further arguments to be passed to the glm function.

References

Polinelli, A., V. Vinciotti and E.C. Wit. (2026). "Causal generalized linear models via Pearson risk invariance" Journal of Causal Inference.

Examples

###################################
#causal Poisson glm#################
n<-1000
set.seed(123)
X1<-rnorm(n,0,1)
Y<-rpois(n,exp(X1))
X2<-log(Y+1)+rnorm(n,0,0.3)
data<-data.frame(X1, X2, Y)
cm_all<-cglm(Y ~ X1+X2,"poisson",data,pval="chi-square",search="all")
cm_all$model.opt
cm_step<-cglm(Y ~ X1+X2,"poisson",data,pval="chi-square",search="stepwise")
cm_step$model.opt
# \donttest{
##########################
#causal logistic glm#######
n<-2000
set.seed(123)
X1<-rnorm(n,0,1)
Y<-rbinom(n,1,exp(X1)/(1+exp(X1)))
flip<-rbinom(n,1,0.1)
X2<-(1-flip)*Y+rnorm(n,0,0.3)
data<-data.frame(X1, X2, Y)
set.seed(1)
cm_all<-cglm(Y ~ X1+X2,"binomial",data,pval="bootstrap",search="all")
cm_all$model.opt
set.seed(1)
cm_step<-cglm(Y ~ X1+X2,"binomial",data,pval="bootstrap",search="stepwise")
cm_step$model.opt
#bigger simulation with 5 covariates
set.seed(12)
n<-3000
X1<-rnorm(n,0,1)
X2<-rnorm(n,X1,0.5)
X3<-rnorm(n,0,1)
X4<-rnorm(n,X2,.5)
Y<-rbinom(n,1,exp(.8*X2-.9*X3)/(1+exp(.8*X2-.9*X3)))
flip<-rbinom(n,1,0.1)
X5<-(1-flip)*Y+flip*(1-Y)+rnorm(n,0,.3)
dat<-data.frame(X1, X2, X3, X4, X5,Y)
set.seed(1)
mod.all <-cglm(Y~X1+X2+X3+X4+X5,"binomial",dat,pval="bootstrap",search="all")
mod.all$model.opt
set.seed(1)
mod.step <-cglm(Y~X1+X2+X3+X4+X5,"binomial",dat,pval="bootstrap",search="stepwise")
mod.step$model.opt
# }