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Re-randomization strategies for balancing covariates using pre-experimental longitudinal data
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.ORCID iD: 0000-0002-1260-7737
2019 (English)Report (Other academic)
Abstract [en]

This paper considers experimental design based on the strategy of rerandomization to increase the effciency in experiments. Two aspects of rerandomization are addressed. First, we propose a two-stage allocation sample scheme for randomization inference to the units of the experiments in balanced experiments that guarantees that the difference-in-mean estimator is an unbiased estimator of SATE for any experiment, conserves the exactness of randomization inference, and halves the time consumption of the rerandomization design. Second, we propose a rank-based covariate balance measure which can take into account the estimated relative weight of each covariate. Several strategies for estimating these weights using pre-experimental data are proposed. Using Monte Carlo simulations, the proposed strategies are compared to complete randomization and Mahalanobis-based rerandomization. An empirical example is given where the power of a mean difference test of the electricity consumption of 54 households is increased by 99%, in comparison to complete randomization, using one of the proposed designs based on high frequency longitudinal electricity consumption data.

Place, publisher, year, edition, pages
Department of Statistics, Uppsala University , 2019. , p. 38
Series
Working paper / Department of Statistics, Uppsala University ; 2018:4
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-364864OAI: oai:DiVA.org:uu-364864DiVA, id: diva2:1260933
Note

Title of the first published version of the report was: Experimental design using longitudinal data

A revised version with a new title was published 2019-01-25.

A second revised version was published 2019-09-30.

Available from: 2018-11-05 Created: 2018-11-05 Last updated: 2019-09-30Bibliographically approved
In thesis
1. Causal Inference in Observational Studies and Experiments: Theory and Applications
Open this publication in new window or tab >>Causal Inference in Observational Studies and Experiments: Theory and Applications
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis consists of six papers that study the design of observational studies and experiments.

Paper I proposes strategies to consistently estimate the average treatment effect of the treated using information derived from a large number of pre-treatment measurements of the outcome. The key to this strategy is to use two-level time-series model estimates to summarize the inter-unit heterogeneity in the sample. It is illustrated how this approach is in line with the conventional identifying assumptions, and how sensitivity analyses of several key assumptions can be performed.

Paper II contains an empirical application of the identification strategy proposed in Paper I. This study provides the first causal analysis of the demand response effects of a billing demand charge involuntarily introduced to small and medium sized electricity users.

Paper III proposes strategies for rerandomization. First, we propose a two-stage allocation sample scheme for randomization inference to the units in balanced experiments that guarantees that the difference-in-means estimator is an unbiased estimator of the sample average treatment effect for any experiment, conserves the exactness of randomization inference, and halves the time consumption of the rerandomization design. Second, we propose a rank-based covariate-balance measure which can take into account the estimated relative weight of each covariate.

Paper IV discusses the concept of optimal rerandomization. It is shown that depending on whether inference is to be drawn to the units of the sample or the population, the notion of optimal differs. We show that it is often advisable to aim for a design that is optimal for inference to the units of the sample, as such a design is often near-optimal also for inference to the units of the population.

Paper V summarizes the current knowledge on asymptotic inference for rerandomization designs and proposes some simplifications for practical applications. Drawing on previous work, we show that the non-normal sampling distribution of the difference-in-means test statistic approaches normal as the rerandomization criterion approaches zero. Furthermore, the difference between the correct non-normal distribution and the proposed approximation based on a normal distribution is in many situations negligible even for near optimal rerandomization criteria.

Paper VI investigates and clarifies the relation between the traditional blocked designs and rerandomization. We show that blocking and rerandomization is very similar, and in some special cases identical. Moreover, it is shown that combining blocking and rerandomization is always at least as efficient as using only rerandomization, but the difference is in many cases small.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 36
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Social Sciences, ISSN 1652-9030 ; 172
Keywords
experimental design, identification, observational studies, rerandomization
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:uu:diva-393810 (URN)978-91-513-0762-6 (ISBN)
Public defence
2019-12-06, Hörsal 2, Ekonomikum, Kyrkogårdsgatan 10, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2019-11-05 Created: 2019-09-27 Last updated: 2019-11-27

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Johansson, PerSchultzberg, Mårten

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