| Title: | 'Gaines and Kuklinski (2011)' Estimators for Hybrid Experiments |
|---|---|
| Description: | Implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by '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, <doi:10.1111/j.1540-5907.2011.00518.x.>.' |
| Authors: | Thomas J. Leeper [aut, cre] |
| Maintainer: | Thomas J. Leeper <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.3 |
| Built: | 2025-02-24 04:47:51 UTC |
| Source: | https://github.com/leeper/gk2011 |
Gaines and Kuklinski (2011) Estimators for Hybrid Experiments
Thomas J. Leeper <[email protected]>
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.
Subset of data from Gaines and Kuklinski (2011)
ajpsajps
The treatment indicator, where 1=treatment, 2=control, 3=chose treatment, 4=chose control.
A “feeling thermometer” toward John McCain.
A “feeling thermometer” toward Barack Obama.
An indicator of party identification, where -1=Republican, 0=Independent, 1=Democrat.
This dataset contains a subset of variables, extracted from the dataset used by Gaines and Kuklinski (2011).
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.
data(ajps) # replicate Gaines and Kuklinski (2011) Table 2 pmean <- function(x) sprintf("%0.1f", mean(x)) cbind( # Democrats aggregate(cbind(therm.mccain, therm.obama) ~ tr, data = ajps[ajps$pid == 1, ], FUN = pmean)[, 1:3], n_dem = aggregate(therm.obama ~ tr, data = ajps[ajps$pid == 1, ], FUN = length)[, 2], # Republicans aggregate(cbind(therm.mccain, therm.obama) ~ tr, data = ajps[ajps$pid == -1, ], FUN = pmean)[, 2:3], n_rep = aggregate(therm.obama ~ tr, data = ajps[ajps$pid == -1, ], FUN = length)[, 2] ) # effects for McCain among Democrats with(ajps[ajps$pid == 1, ], { estimate(rand = tr %in% 1:2, tr = tr %in% c(1,3), y = therm.mccain) }) # effects for McCain among Republicans with(ajps[ajps$pid == -1, ], { estimate(rand = tr %in% 1:2, tr = tr %in% c(1,3), y = therm.mccain) }) # effects for Obama among Democrats with(ajps[ajps$pid == 1, ], { estimate(rand = tr %in% 1:2, tr = tr %in% c(1,3), y = therm.obama) }) # effects for Obama among Republicans with(ajps[ajps$pid == -1, ], { estimate(rand = tr %in% 1:2, tr = tr %in% c(1,3), y = therm.obama) })data(ajps) # replicate Gaines and Kuklinski (2011) Table 2 pmean <- function(x) sprintf("%0.1f", mean(x)) cbind( # Democrats aggregate(cbind(therm.mccain, therm.obama) ~ tr, data = ajps[ajps$pid == 1, ], FUN = pmean)[, 1:3], n_dem = aggregate(therm.obama ~ tr, data = ajps[ajps$pid == 1, ], FUN = length)[, 2], # Republicans aggregate(cbind(therm.mccain, therm.obama) ~ tr, data = ajps[ajps$pid == -1, ], FUN = pmean)[, 2:3], n_rep = aggregate(therm.obama ~ tr, data = ajps[ajps$pid == -1, ], FUN = length)[, 2] ) # effects for McCain among Democrats with(ajps[ajps$pid == 1, ], { estimate(rand = tr %in% 1:2, tr = tr %in% c(1,3), y = therm.mccain) }) # effects for McCain among Republicans with(ajps[ajps$pid == -1, ], { estimate(rand = tr %in% 1:2, tr = tr %in% c(1,3), y = therm.mccain) }) # effects for Obama among Democrats with(ajps[ajps$pid == 1, ], { estimate(rand = tr %in% 1:2, tr = tr %in% c(1,3), y = therm.obama) }) # effects for Obama among Republicans with(ajps[ajps$pid == -1, ], { estimate(rand = tr %in% 1:2, tr = tr %in% c(1,3), y = therm.obama) })
Estimators for Hybrid Experiments
estimate(rand, tr, y, iterations = 5000L)estimate(rand, tr, y, iterations = 5000L)
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. |
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:\
The effect for those inclined to choose treatment is given by:\
where 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:\
By definition, the sample average treatment effect is an average of the other two effects.
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 .
Thomas J. Leeper <[email protected]>
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.
# 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$y)# create fake data set.seed(12345) d <- data.frame(rand = c(rep(1, 200), rep(0, 100)), tr = c(rep(0, 100), rep(1, 100), rep(0, 37), rep(1, 63)), y = c(rnorm(100), rnorm(100) + 1, rnorm(37), rnorm(63) + 3)) # estimate effects estimate(rand = d$rand, tr = d$tr, y = d$y)