boundsdata | R Documentation |

## Example Data for the Design Functions

### Description

A random subsample of the simulated data used in Imai, Tingley, and
Yamamoto (2012). The data contains 1000 rows and 7 columns with no missing
values.

### Usage

boundsdata

### Format

A data frame containing the following variables, which are
interpreted as results from a hypothetical randomized trial. See the source
for a full description.

- out:
The binary outcome variable under the parallel design.

- out.enc:
The binary outcome variable under the parallel
encouragement design.

- med:
The binary mediator under the parallel design.

- med.enc:
The binary mediator under the parallel encouragement
design.

- ttt:
The binary treatment variable.

- manip:
The design indicator, or the variable indicating whether the
mediator is manipulated under the parallel design.

- enc:
The trichotomous encouragement variable under the parallel
encouragement design. Equals 0 if subject received no encouragement; 1 if
encouraged for the mediator value of 1; and -1 if encouraged for the
mediator value of 0.

### Details

Conditioning on 'manip' = 0 will simulate a randomized trial under
the single experiment design, where 'out' and 'med' equal observed outcome
and mediator values, respectively.

Unconditionally, using 'out', 'med', 'ttt' and 'manip' will simulate an
experiment under the parallel design.

The 'out.enc' and 'med.enc' variables represent the outcome and mediator
values observed when subjects received the encouragement indicated in
'enc'. Therefore, using 'out.enc', 'med.enc', 'ttt' and 'enc' will simulate
an experiment under the parallel encouragement design.

Note that all the observed responses are generated from an underlying
distribution of potential outcomes and mediators (not shown in this
dataset) satisfying the assumptions described in Imai, Tingley and
Yamamoto (2012). The full simulation code is available as a companion
replication archive for the article.

### Source

Imai, K., Tingley, D. and Yamamoto, T. (2012) Experimental Designs
for Identifying Causal Mechanisms. Journal of the Royal Statistical
Society, Series A (Statistics in Society).