iv_factorial {factiv} | R Documentation |
IV Estimation of 2^K Factorial Design
Description
Estimates principal stratum-specific effects and interactions in a 2^K factorial experiment
Usage
iv_factorial(formula, data, subset, method = "lm", 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. |
method |
character indiciating if the estimator should be
|
level |
the confidence level required. |
Details
This function estimates treatment effects for 2^K factorial experiments in the face of noncompliance on all factors. A monotonicity assumption is assumed for both treatment-instrument pairs, along with treatment exclusion. See Blackwell (2017) for more details on those assumptions.
The procedure uses iterative generalized method of moments (GMM) to estimate both the proportions of each compliance class (also known as principal strata) and the average potential outcomes within those classes. It also provides estimates of several one-way, joint, and interactive treatment effects within these classes.
Under the above assumptions, the compliance classes are the
product of the compliance classes for each treatment-instrument
pair. For instance, "cc"
is the class that would comply
with both treatments, "ca"
is the class that would comply
with the first treatment and always take the second treatment, and
"cn"
is the class that would comply with the first
treatment and never take the second treatment. Finally, note that
treatment effects are only well-defined for compliance classes for
which there is compliance on at least one treatment.
Value
A list of class iv_factorial
that contains the following
components:
rho |
vector of estimated compliance class probabilities. |
psi |
vector of the estimated conditional mean of the outcome within the compliance classes. |
vcov |
estimated asymptotic variance matrix of the combined
|
pcafe_est |
vector of estimated main effects of each factor among perfect compliers. |
pcafe_se |
vector of estimated standard errors for the
estimated effects in |
pcafe_cis |
a matrix of confidence intervals for the PCAFE estimates. |
level |
the confidence level of the returned confience intervals. |
Author(s)
Matt Blackwell
References
Matthew Blackwell (2017) Instrumental Variable Methods for Conditional Effects and Causal Interaction in Voter Mobilization Experiments, Journal of the American Statistical Association, 112:518, 590-599, doi: 10.1080/01621459.2016.1246363
Matthew Blackwell and Nicole Pashley (2020) "Noncompliance in Factorial Experiments." Working paper.
Examples
data(newhaven)
out <- iv_factorial(turnout_98 ~ inperson + phone | inperson_rand
+ phone_rand, data = newhaven)
summary(out)