ATEbounds {experiment} | R Documentation |
This function computes the sharp bounds on the average treatment effect when some of the outcome data are missing. The confidence intervals for the bounds are also computed.
ATEbounds(formula, data = parent.frame(), maxY = NULL, minY = NULL, alpha = 0.05, n.reps = 0, strata = NULL, ratio = NULL, survey = NULL, ...)
formula |
A formula of the form Y ~ X where Y is
the name of the outcome variable and X is the
name of the (randomized) treatment variable. X should be a
factor variable but its value can take more than two levels. The missing
values for Y should be coded as NA .
|
data |
A data frame containing the relevant variables. |
maxY |
A scalar. The maximum value of the outcome variable. The default is the maximum sample value. |
minY |
A scalar. The minimum value of the outcome variable. The default is the minimum sample value. |
alpha |
A positive scalar that is less than or equal to 0.5. This will
determine the (1-alpha ) level of confidence intervals. The
default is 0.05 .
|
strata |
The variable name indicating strata. If this is specified, the
quantities of interest will be first calculated within each strata
and then aggregated. The default is NULL .
|
ratio |
A J times M matrix of probabilities where J is the
number of strata and M is the number of treatment and control
groups. Each element of the matrix specifies the probability of a
unit falling into that category. The default is NULL in which
case the sample estimates of these probabilities are used for
computation.
|
survey |
The variable name for survey weights. The default is NULL .
|
n.reps |
A positive integer. The number of bootstrap replicates used for the construction of confidence intervals via B-method of Berran (1988). If it equals zero, the confidence intervals will not be constructed. |
... |
The arguments passed to other functions. |
For the details of the method implemented by this function, see the references.
A list of class ATEbounds
which contains the following items:
call |
The matched call. |
Y |
The outcome variable. |
D |
The treatment variable. |
bounds |
The point estimates of the sharp bounds on the average treatment effect. |
bounds.Y |
The point estimates of the sharp bounds on the outcome variable within each treatment/control group. |
bmethod.ci |
The B-method confidence interval of the bounds on the average treatment effect. |
bonf.ci |
The Bonferroni confidence interval of the bounds on the average treatment effect. |
bonf.ci.Y |
The Bonferroni confidence interval of the bounds on the outcome variable within each treatment/control group. |
bmethod.ci.Y |
The B-method confidence interval of the bounds on the outcome variable within each treatment/control group. |
maxY |
The maximum value of the outcome variable used in the computation. |
minY |
The minimum value of the outcome variable used in the computation. |
nobs |
The number of observations. |
nobs.Y |
The number of observations within each treatment/control group. |
ratio |
The probability of treatment assignment (within each strata if
strata is specified) used in the computation.
|
Kosuke Imai, Department of Politics, Princeton University kimai@Princeton.Edu, http://imai.princeton.edu;
Horowitz, Joel L. and Charles F. Manski. (1998). “Censoring of Outcomes and Regressors due to Survey Nonresponse: Identification and Estimation Using Weights and Imputations.” Journal of Econometrics, Vol. 84, pp.37-58.
Horowitz, Joel L. and Charles F. Manski. (2000). “Nonparametric Analysis of Randomized Experiments With Missing Covariate and Outcome Data.” Journal of the Americal Statistical Association, Vol. 95, No. 449, pp.77-84.
Harris-Lacewell, Melissa, Kosuke Imai, and Teppei Yamamoto. (2007). “Racial Gaps in the Responses to Hurricane Katrina: An Experimental Study”, Technical Report. Department of Politics, Princeton University.