iv_finite_factorial {factiv} | R Documentation |
Finite-Sample IV Estimation of 2^K Factorial Design
Description
Estimate main effect IV ratios for 2^K factorial experiments
Usage
iv_finite_factorial(formula, data, subset, level = 0.95)
Arguments
formula |
formula specification of the factorial design with
noncompliance. The right-hand side of the formula should have
two components separated by the |
data |
a data.frame on which to apply the |
subset |
subset of the data to pass to estimation. |
level |
the confidence level required. |
Details
This function estimates the ratio of the effect of treatment assignment on the outcome to the effect of treatment assignment on treatment uptake in 2^K factorial experiments. The approach uses finite sample asymptotic inference to generate confidence intervals.
Value
A list of class iv_finite_factorial
that contains the
following components:
tau |
a vector of estimated effect ratios for each factor. |
tau_cis |
a matrix of confidence intervals for each effect ratio. This matrix has 4 columns because it is possible to have disjoint confidence intervals in this method. |
tau_y |
a vector of the estimated effects on the outcome. |
v_tau_y |
the estimated sample variances of the effects on the outcome. |
tau_d |
a vector of the estimated effects on treatment uptake. |
v_tau_y |
the estimated sample variances of the effects on treatment uptake. |
level |
the confidence level of |
Author(s)
Matt Blackwell
References
Matthew Blackwell and Nicole Pashley (2021) "Noncompliance in Factorial Experiments." Working paper.
Examples
data(newhaven)
out <- iv_finite_factorial(turnout_98 ~ inperson + phone | inperson_rand
+ phone_rand, data = newhaven)
out
joint <- iv_finite_factorial(turnout_98 ~ inperson + phone |
inperson_rand + phone_rand, data = newhaven)
joint