The P-LOOP Estimator: Covariate Adjustment for Paired Experiments

05/21/2019
by   Edward Wu, et al.
0

In paired experiments, participants are grouped into pairs with similar characteristics, and one observation from each pair is randomly assigned to treatment. Because of both the pairing and the randomization, the treatment and control groups should be well balanced; however, there may still be small chance imbalances. It may be possible to improve the precision of the treatment effect estimate by adjusting for these imbalances. Building on related work for completely randomized experiments, we propose the P-LOOP (paired leave-one-out potential outcomes) estimator for paired experiments. We leave out each pair and then impute its potential outcomes using any prediction algorithm. The imputation method is flexible; for example, we could use lasso or random forests. While similar methods exist for completely randomized experiments, covariate adjustment methods in paired experiments are relatively understudied. A unique trade-off exists for paired experiments, where it can be unclear whether to factor in pair assignments when making adjustments. We address this issue in the P-LOOP estimator by automatically deciding whether to account for the pairing when imputing the potential outcomes. By addressing this trade-off, the method has the potential to improve precision over existing methods.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

10/18/2021

Robustly leveraging the post-randomization information to improve precision in the analyses of randomized clinical trials

In randomized clinical trials, repeated measures of the outcome are rout...
09/21/2021

Rejective Sampling, Rerandomization and Regression Adjustment in Survey Experiments

Classical randomized experiments, equipped with randomization-based infe...
04/21/2020

Propensity Score Weighting for Covariate Adjustment in Randomized Clinical Trials

Imbalance in baseline characteristics due to chance is common in randomi...
08/12/2020

Covariate Balancing Based on Kernel Density Estimates for Controlled Experiments

Controlled experiments are widely used in many applications to investiga...
03/24/2021

Pair-switching rerandomization

Rerandomization discards assignments with covariates unbalanced in the t...
07/15/2021

Covariate adjustment in randomised trials: canonical link functions protect against model mis-specification

Covariate adjustment has the potential to increase power in the analysis...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.