cv.causalBoosting: Fit a causal boosting model with cross validation

In saberpowers/causalLearning: Methods for heterogeneous treatment effect estimation

Description

Fit a causal boosting model with cross validation

Usage

cv.causalBoosting(x, tx, y, num.trees = 500, maxleaves = 4, eps = 0.01,
  splitSpread = 0.1, type.measure = c("effect", "response"), nfolds = 5,
  foldid = NULL, propensity = FALSE, stratum = NULL, isConstVar = TRUE)

Arguments

x	matrix of covariates
tx	vector of treatment indicators (0 or 1)
y	vector of response values
num.trees	number of shallow causal trees to build
maxleaves	maximum number of leaves per causal tree
eps	learning rate
splitSpread	how far apart should the candidate splits be for the causal trees? (e.g. splitSpread = 0.1) means we consider 10 quantile cutpoints as candidates for making split
type.measure	loss to use for cross validation: 'response' returns mean-square error for predicting response in each arm. 'effect' returns MSE for treatment effect using honest over-fit estimation.
nfolds	number of cross validation folds
foldid	vector of fold membership
propensity	logical: should propensity score stratification be used?
stratum	optional vector giving propensity score stratum for each observation (only used if propensity = TRUE)
isConstVar	logical: for the causal tree splitting criterion (T-statistc), should it be assumed that the noise variance is the same in treatment and control arms?

Value

an object of class cv.causalBoosting which is an object of class causalBoosting with these additional attributes:

• num.trees.min: number of trees with lowest CV error

• cvm: vector of mean CV error for each number of trees

• cvsd: vector of standard errors for mean CV errors

Examples

# Randomized experiment example

n = 100 # number of training-set patients to simulate
p = 10  # number of features for each training-set patient

# Simulate data
x = matrix(rnorm(n * p), nrow = n, ncol = p) # simulate covariate matrix
tx_effect = x[, 1] + (x[, 2] > 0) # simple heterogeneous treatment effect
tx = rbinom(n, size = 1, p = 0.5) # random treatment assignment
y = rowMeans(x) + tx * tx_effect + rnorm(n, sd = 0.001) # simulate response

# Estimate causal boosting model with cross-validation
fit_cv = causalLearning::cv.causalBoosting(x, tx, y)
fit_cv$num.trees.min.effect # number of trees chosen by cross-validation
pred_cv = predict(fit_cv, newx = x)

# Visualize results
plot(tx_effect, pred_cv, main = 'Causal boosting w/ CV',
 xlab = 'True treatment effect', ylab = 'Estimated treatment effect')
abline(0, 1, lty = 2)

saberpowers/causalLearning documentation built on May 30, 2019, 8:26 a.m.