MDEI {MDEI}

MDEI function

Description

Implements the Method of Direct Estimation and Inference

Usage

MDEI(
  y,
  treat,
  X,
  splits = 10,
  alpha = 0.9,
  samplesplit = TRUE,
  conformal = TRUE,
  nthreads.ranger = NULL,
  verbose = TRUE
)

Arguments

y

The outcome variable, a vector.

treat

The treatment variable, a vector.

X

A matrix of covariates.

splits

Number of repeated cross-fitting steps to implement.

alpha

The desired level of the confidence band.

samplesplit

Whether to use a sample splitting approach. Default is TRUE.

conformal

Whether to generate a conformal bands or use a critical value from the normal approximation. Default is TRUE..

nthreads.ranger

Number of threads used internally by the ranger function for random forests. Default is NULL.

verbose

An optional logical value. If TRUE information on the number of split samples completed is printed. Default is TRUE.

Value

tau.est

The estimated marginal effect.

CIs.tau

Upper and lower values of conformal confidence band.

critical.values

Conformal critical values.

Ey.x

Mean of outcome given only covariates.

coefficients

The list of all nonparametric bases and the proportion of sample splits that they were selected.

internal

Internal objects used for development and diagnostics.

References

Ratkovic, Marc and Dustin Tingley. 2023. "Estimation and Inference on Nonlinear and Heterogeneous Effects." The Journal of Politics.

Examples

n <- 100

X <- matrix(rnorm(n*1), nrow = n)
treat <- rnorm(n)
y <- treat^2 + X[,1] + rnorm(n)

# Be sure to run with more splits than this.  We recommend
# at least 10-50 initially, for exploratory analyses, with several hundred for 
# publication quality. For large sample sizes, these numbers may be adjusted down.
# These are only recommendations.
# Threads are set to 1 to pass CRAN checks, but we suggest leaving it at the default
# which ranger takse as the total number available.
set.seed(1)
m1 <- MDEI(y, treat, X, splits=1, alpha=.9, nthreads.ranger = 1)

# Accuracy
cor(m1$tau.est, treat*2)
cor(m1$theta.est, treat^2)

# Coverage
mean(apply(m1$CIs.tau-2*treat,1,prod)<0)