Rerandomization and Regression Adjustment

Li, Xinran; Ding, Peng

doi:10.1111/rssb.12353

Cited by 66 publications

(55 citation statements)

References 57 publications

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“…While a paired design is often effective at balancing covariates between the treatment and control groups, it may still be helpful to make adjustments for remaining covariate imbalances. Similar situations can occur with other study designs; for example, covariate adjustments may be helpful in rerandomized trials (see Li & Ding, 2020). Perhaps in part because covariate balance is addressed through experimental design, covariate adjustment methods in paired experiments are relatively understudied.…”

Section: Introductionmentioning

confidence: 89%

Design-Based Covariate Adjustments in Paired Experiments

Gagnon-Bartsch

2020

Journal of Educational and Behavioral Statistics

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In paired experiments, participants are grouped into pairs with similar characteristics, and one observation from each pair is randomly assigned to treatment. The resulting treatment and control groups should be well-balanced; however, there may still be small chance imbalances. Building on work for completely randomized experiments, we propose a design-based method to adjust for covariate imbalances in paired experiments. We leave out each pair and impute its potential outcomes using any prediction algorithm such as lasso or random forests. This method addresses a unique trade-off that exists for paired experiments. By addressing this trade-off, the method has the potential to improve precision over existing methods.

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Section: Introductionmentioning

confidence: 89%

Design-Based Covariate Adjustments in Paired Experiments

Gagnon-Bartsch

2020

Journal of Educational and Behavioral Statistics

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“…This result also suggests that the proposed method can also improve the precision of generalizing effect estimation from an RCT to a target population, especially when the outcomes are largely affected by some covariates. Although by randomization these covariates follow the same distribution across the treated and control groups, under finite samples covariate imbalance often occurs due to sampling error (Li and Ding, 2020). The proposed method provides a strategy to tackle such imbalance without resorting to regression adjustment.…”

Section: Theoretical Propertiesmentioning

confidence: 99%

Entropy Balancing for Causal Generalization with Target Sample Summary Information

Chen¹,

Chen²,

Yu³

2022

Preprint

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In this paper, we focus on estimating the average treatment effect (ATE) of a target population when individual-level data from a source population and summary-level data (e.g., first or second moments of certain covariates) from the target population are available. In the presence of heterogeneous treatment effect, the ATE of the target population can be different from that of the source population when distributions of treatment effect modifiers are dissimilar in these two populations, a phenomenon also known as covariate shift. Many methods have been developed to adjust for covariate shift, but most require individual covariates from the target population. We develop a weighting approach based on summary-level information from the target population to adjust for possible covariate shift in effect modifiers. In particular, weights of the treated and control groups within the source population are calibrated by the summary-level information of the target population. In addition, our approach also seeks additional covariate balance between the treated and control groups in the source population. We study the asymptotic behavior of *

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Section: Related Workmentioning

confidence: 99%

“…More generally, there is a vast literature on using pre-assignment covariates for regression adjustment to increase estimation efficiency [11,13,26,27,30,34]. Our method is based on the augmentation idea of CUPED [6](see also [26]), applied to the one-sided triggering context, and is a general approach that can be used on top of any pre-assignment covariate regression adjustment. In particular, our approach has the flexibility to incorporate in-experiment observations for covariate adjustment without introducing bias, though there is a tradeoff in the amount of variance reduced.…”

Section: Related Workmentioning

confidence: 99%

Zero to Hero: Exploiting Null Effects to Achieve Variance Reduction in Experiments with One-sided Triggering

Deng¹,

Yuan²,

Salama-Manteau³

2021

Preprint

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In online experimentation, trigger-dilute analysis is an approach to obtain more precise estimates of intent-to-treat (ITT) effects when the intervention is only exposed, or "triggered", for a small subset of the population. Trigger-dilute analysis cannot be used for estimation when triggering is only partially observed. In this paper, we propose an unbiased ITT estimator with reduced variance for cases where triggering status is only observed in the treatment group. Our method is based on the efficiency augmentation idea of CUPED and draws upon identification frameworks from the principal stratification and instrumental variables literature. The unbiasedness of our estimation approach relies on a testable assumption that an augmentation term used for covariate adjustment equals zero in expectation. When this augmentation term fails a mean-zero test, we show how our estimator can incorporate in-experiment observations to reduce the augmentation's bias, by sacrificing the amount of variance reduced. This provides an explicit knob to trade off bias with variance. We demonstrate through simulations that our estimator can remain unbiased and achieve precision improvements as good as if triggering status were fully observed, and in some cases outperforms trigger-dilute analysis.

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Rerandomization and Regression Adjustment

Cited by 66 publications

References 57 publications

Design-Based Covariate Adjustments in Paired Experiments

Design-Based Covariate Adjustments in Paired Experiments

Entropy Balancing for Causal Generalization with Target Sample Summary Information

Zero to Hero: Exploiting Null Effects to Achieve Variance Reduction in Experiments with One-sided Triggering

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