add_spl_est {causaldrf}

The additive spline estimator

Description

This function estimates the ADRF with an additive spline estimator described in Bia et al. (2014).

Usage

add_spl_est(Y,
            treat,
            treat_formula,
            data,
            grid_val,
            knot_num,
            treat_mod,
            link_function,
            ...)

Arguments

Y

is the the name of the outcome variable contained in data.

treat

is the name of the treatment variable contained in data.

treat_formula

an object of class "formula" (or one that can be coerced to that class) that regresses treat on a linear combination of X: a symbolic description of the model to be fitted.

data

is a dataframe containing Y, treat, and X.

grid_val

contains the treatment values to be evaluated.

knot_num

is the number of knots used in outcome model

treat_mod

a description of the error distribution to be used in the model for treatment. Options include: "Normal" for normal model, "LogNormal" for lognormal model, "Sqrt" for square-root transformation to a normal treatment, "Poisson" for Poisson model, "NegBinom" for negative binomial model, "Gamma" for gamma model.

link_function

is either "log", "inverse", or "identity" for the "Gamma" treat_mod.

...

additional arguments to be passed to the outcome regression fitting function.

Details

This function estimates the ADRF using additive splines in the outcome model described in Bia et al. (2014).

Value

add_spl_est returns an object of class "causaldrf", a list that contains the following components:

param

parameter estimates for a add_spl fit.

t_mod

the result of the treatment model fit.

out_mod

the result of the outcome model fit.

call

the matched call.

References

Schafer, J.L., Galagate, D.L. (2015). Causal inference with a continuous treatment and outcome: alternative estimators for parametric dose-response models. Manuscript in preparation.

Bia, Michela, et al. (2014). A Stata package for the application of semiparametric estimators of dose response functions. Stata Journal 14.3, 580-604.

See Also

nw_est, iw_est, hi_est, gam_est, bart_est, etc. for other estimates.

t_mod, overlap_fun to prepare the data for use in the different estimates.

Examples

## Example from Schafer (2015).
example_data <- sim_data
add_spl_list <- add_spl_est(Y = Y,
            treat = T,
            treat_formula = T ~ B.1 + B.2 + B.3 + B.4 + B.5 + B.6 + B.7 + B.8,
            data = example_data,
            grid_val = seq(8, 16, by = 1),
            knot_num = 3,
            treat_mod = "Normal")


sample_index <- sample(1:1000, 100)
plot(example_data$T[sample_index],
      example_data$Y[sample_index],
      xlab = "T",
      ylab = "Y",
      main = "additive spline estimate")

lines(seq(8, 16, by = 1),
      add_spl_list$param,
      lty = 2,
      lwd = 2,
      col = "blue")
legend('bottomright',
        "additive spline estimate",
        lty=2,
        lwd = 2,
        col = "blue",
        bty='Y', cex=1)

rm(example_data, add_spl_list, sample_index)

## See Vignette for more examples.