Description Usage Arguments Details Value Note Author(s) References See Also Examples
Model-free selection of covariates in high dimensions under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011) and VanderWeele and Shpitser (2011). Confounder selection can be performed via either Markov/Bayesian networks, random forests or LASSO.
1 2 3 4 5 |
T |
A vector, containing |
Y |
A vector of observed outcomes. |
X |
A matrix or data frame containing columns of covariates. The covariates may be a mix of continuous, unordered discrete
(to be specified in the data frame using |
type |
The type of method used for selection. The networks algorithms are |
betahat |
If |
parallel |
If |
Simulate |
If data is to be simulated according to one of the designs in Häggström (2017) then |
N |
If Simulate=TRUE, |
Setting |
If Simulate=TRUE, |
rep |
If Simulate=TRUE, |
Models |
If Simulate=TRUE, |
alpha |
A numeric value, the target nominal type I error rate (tuning parameter) for |
mmhc_score |
The score to use for |
See Häggström (2017).
cov.sel.high
returns a list with the following content:
X.T |
The set of covariates targeting the subset containing all causes of |
Q.0 |
The set of covariates targeting the subset of |
Q.1 |
The set of covariates targeting the subset of |
Q |
Union of Q.0 and Q.1. |
X.0 |
The set of covariates targeting the subset containing all causes of |
X.1 |
The set of covariates targeting the subset containing all causes of |
X.Y |
Union of X.0 and X.1. |
Z.0 |
The set of covariates targeting the subset of |
Z.1 |
The set of covariates targeting the subset of |
Z |
Union of Z.0 and Z.1. |
X.TY |
Union of X.T and X.Y, the set of covariates targeting the subset containing all causes of |
cardinalities |
The cardinalities of each selected subset. |
est_psm |
The PSM estimate of the average causal effect, for the full covariate vector and each selected subset. |
se_psm |
The Abadie-Imbens standard error for the PSM estimate of the average causal effect, for the full covariate vector and each selected subset. |
est_tmle |
The TMLE estimate of the average causal effect, for the full covariate vector and each selected subset. |
se_psm |
The influence-curve based standard error for the TMLE estimate of the average causal effect, for the full covariate vector and each selected subset. |
N |
The number of observations. |
Setting |
The Setting used. |
rep |
The number of replications. |
Models |
Models used. |
type |
type used. |
alpha |
alpha used. |
mmhc_score |
score used. |
varnames |
Variable names of the used data. |
Depending on the method type specified cov.sel.high
calls one of the functions mmpc
, mmhc
, randomForest
, cv.glmnet
and, if betahat=TRUE, Match
and tmle
, therefore the packages bnlearn
, randomForest
, glmnet
, Matching
and tmle
are required.
Jenny Häggström, <jenny.haggstrom@umu.se>
de Luna, X., I. Waernbaum, and T. S. Richardson (2011). Covariate selection for the nonparametric estimation of an average treatment effect. Biometrika 98. 861-875
Häggström, J. (2017). Data-Driven Confounder Selection via Markov and Bayesian Networks. ArXiv e-prints.
Nagarajan, R., M. Scutari and S. Lebre. (2013) Bayesian Networks in R with Applications in Systems Biology. Springer, New York. ISBN 978-1461464457.
Scutari, M. (2010). Learning Bayesian Networks with the bnlearn R Package. Journal of Statistical Software, 35, 1-22. URL http://www.jstatsoft.org/v35/i03/.
Sekhon, J.S. (2011). Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R. Journal of Statistical Software, 42, 1-52. URL http://www.jstatsoft.org/v42/i07/.
bnlearn-package
, randomForest
, cv.glmnet
, Match
and tmle
1 2 3 4 5 6 7 8 9 10 11 | ##Use simulated data, select subsets using mmpc
ans<-cov.sel.high(type="mmpc",N=1000, rep=2, Models="Linear", betahat=FALSE, mmhc_score="aic")
##Use simulated data, select subsets using mmpc and estimate ACEs, parallell version
#library(doParallel)
#library(doRNG)
#cl <- makeCluster(4)
#registerDoParallel(cl)
#ans<-cov.sel.high(type="mmpc", parallel=TRUE, N=500, rep=10, Models="Linear", mmhc_score="aic")
#stopCluster(cl)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.