estimate {GK2011} | R Documentation |
estimate
Description
Estimators for Hybrid Experiments
Usage
estimate(rand, tr, y, iterations = 5000L)
Arguments
rand |
An integer or logical vector specifying whether each observation is from the random (1) or self-selection (0) arm of the experiment. |
tr |
An integer or logical vector specifying whether each observation was treated (1) or control (0), regardless of the arm of the experiment. |
y |
A numeric vector specifying outcome values. |
iterations |
An integer specifying the number of bootstrap iterations used to estimate standard errors. |
Details
The package provides R implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by Gaines and Kuklinski (2011). These functions estimate local average treatment effects for unobserved population subgroups inclined and disinclined to be treated, as revealed by a three-condition (two-arm) experimental design. In the design, participants are randomly assigned to one of three conditions: 1) treatment (T), 2) control (C), or 3) self-selection (S) of treatment or control. The design enables the estimation of three treatment effects:
First, the sample average treatment effect is estimated from conditions (1) and (2) as:\
\hat{t} = \bar{Y}_{T} - \bar{Y}_{C}
The effect for those inclined to choose treatment is given by:\
\hat{t}_s = \frac{\bar{Y}_{S} - \bar{Y}_{C}}{\hat{\alpha}}
where\hat{\alpha}
is the observed proportion of individuals in group S that choose T (rather than C).The effect for those disinclined to choose treatment (or, equivalently, inclined to choose control) is given by:\
\hat{t}_n = \frac{\bar{Y}_{T} - \bar{Y}_{S}}{1-\hat{\alpha}}
By definition, the sample average treatment effect is an average of the other two effects.
Value
A data.frame containing the following variables:
-
Effect
, a character vector of effect names (“t”, “t_s”, “t_n”, “naive”) -
Estimate
, a numeric vector of effect estimates -
SE
, a numeric vector of bootstrapped standard errors -
t
, a t-statistic for the effect -
p
, a two-tailed p-value
The return value will also carry an attribute “alpha”, indicating the estimated proportion \alpha
.
Author(s)
Thomas J. Leeper <thosjleeper@gmail.com>
References
Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.
See Also
Examples
# create fake data
set.seed(12345)
d <-
data.frame(rand = c(rep(1, 200), rep(0, 100)),
tr = c(rep(0, 100), rep(1, 100), rep(0, 37), rep(1, 63)),
y = c(rnorm(100), rnorm(100) + 1, rnorm(37), rnorm(63) + 3))
# estimate effects
estimate(rand = d$rand, tr = d$tr, y = d$y)