blockTools
blockTools: Blocking, Assignment, and Diagnosing Interference in Randomized Experiments
Version 0.6.5
Ryan T. Moore and Keith Schnakenberg
Available as a CRAN package
Abstract
Causal inference in social and biomedical research often relies on estimates from randomized experiments.
blockTools offers functionality for designing, conducting, and analyzing aspects of randomized experiments. "Blocking" involves sorting experimental units into homogeneous groups prior to randomization. Randomization then occurs within those groups.
blockTools allows experimentalists to easily
- block experimental units using many background covariates simultaneously (including continuous ones)
- incorporate substantive knowledge via weights on variables
- randomly assign blocked units to treatment conditions within blocks
- detect possible interference between treatment and control units ("contamination", or "spillovers")
- quickly write tables of proposed blocks or experimental assignment protocols to .tex or .csv files
Documentation
The
manual
details the many options associated with this functionality.
Installation
To install and load
blockTools, open R and type
> install.packages("blockTools")
> library(blockTools)
To install the latest version (0.6.5) directly, download the package
(
here) and
install using R CMD INSTALL.
(You might have to rezip the tarball if your downloader unzips it. I.e., if you see
blockTools_0.6.5.tar, you might need to
gzip blockTools_0.6.5.tar to recreate
blockTools_0.6.5.tar.gz.)
Using blockTools
There are 3 primary functions of
blockTools:
block,
assignment, and
diagnose.
- block creates experimental blocks.
- assignment assigns one unit in each block to each treatment condition.
- diagnose detects units of different treatment assignments that are "too close" or "too far away" from each other on some variable.
Helper Functions
You might find two recent helper functions useful:
- createBlockIDs takes an assignment object and creates a
vector of block IDs.
- assg2xBalance interfaces between an
assignment object and xBalance from package RItools.
See the package documentation for more details.
Examples
At the R prompt, type:
> data(x100) ## load the example data
> out <- block(x100, id.vars = "id", block.vars = c("b1", "b2")) ## create blocked pairs
> assg <- assignment(out) ## assign one member of each pair to treatment/control
> diag <- diagnose(assg, x100, id.vars = "id", suspect.var = "b1", suspect.range = c(0,1)) ## detect unit pairs with different treatment assignments that are within 1 unit of each other on variable "b1"
To view the results:
> out$blocks ## blocked pairs
> assg ## assigned pairs
> diag ## pairs with small distances on covariates between them
Speed and Dataset Size
As of version 0.5-1, all blocking is done in C. The
block()
function in that version was tested on a desktop machine (iMac, Intel Core i5, 3.6 GHz, 8 GB) and successfully completed these runs:
| Units |
Variables |
Algorithm |
Time |
| 1000 |
5 |
optGreedy |
0m 1s |
| 5000 |
5 |
optGreedy |
1m 35s |
| 10,000 |
2 |
optGreedy |
12m 53s |
| 10,000 |
5 |
optGreedy |
12m 38s |
| 10,000 |
5 |
naiveGreedy |
0m 43s |
| 20,000 |
5 |
optGreedy |
101m 16s |
| 20,000 |
5 |
naiveGreedy |
3m 59s |
Note on Variable Restrictions
If you use the
valid.var and
valid.range arguments
of the
block function, this may result in fewer than the
maximum possible number of blocks. To see why, consider how
algorithm = "optGreedy" or
algorithm = "naiveGreedy"
would handle distance matrix below. Either would select only the pair
(1,2), rather than, e.g., pairs (1,3) and (2,4), as would
algorithm = "optimal".
| Inf |
2 |
3 |
4 |
| 2 |
Inf |
5 |
6 |
| 3 |
5 |
Inf |
Inf |
| 4 |
6 |
Inf |
Inf |
Citation
If you use this package, please cite the paper
Moore, Ryan T. ``Multivariate Continuous Blocking to Improve Political Science
Experiments''.
Political Analysis, 20(4):460--479, Autumn
2012.
or
Moore, Ryan T. and Sally A. Moore. ``Blocking for Sequential Political Experiments''.
Political Analysis, 21(4):507–523, 2013.
as appropriate. The software can be cited directly as
Moore, Ryan T. and Keith Schnakenberg. "blockTools: Blocking, Assignment, and Diagnosing
Interference in Randomized Experiments", Version 0.6.5, October 2024.
For an application, see:
King, Gary, Emmanuela Gakidou, Nirmala Ravishankar, Ryan T. Moore, Jason Lakin, Manett Vargas, Martha María Téllez-Rojo, Juan Eugenio Hernández Ávila, Mauricio Hernández Ávila and Héctor Hernández Llamas. 2007. "A 'Politically Robust' Experimental Design for Public Policy Evaluation, with Application to the Mexican Universal Health Insurance Program".
Journal of Policy Analysis and Management, 26(3): 479-509.
Previous versions (CHANGELOG):
0.6.5 (2024-10-19)
0.6.4 (2023-02-06)
0.6-3 (2 December 2016)
0.6-2 (8 January 2015)
0.6-1 (22 May 2014)
0.5-8 (8 April 2014)
0.5-7 (30 July 2013)
0.5-6 (1 August 2012)
0.5-5 (11 June 2012)
0.5-4 (7 May 2012)
0.5-3 (4 March 2011)
0.5-2 (16 November 2010)
0.5-1 (6 October 2010)
0.4-1 (28 October 2009)
0.3 (29 April 2009)
0.2 (10 April 2008)