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Using high frequency pre-treatment outcomes to identify causal effects in non-experimental data: Causal effects in non-experimental data
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.ORCID iD: 0000-0002-1260-7737
2018 (English)Report (Other academic)
Abstract [en]

In observational studies it is common to use matching strategies to consistently estimate the average treatment effect of the treated (ATET) under the unconfoundedness assumption of the outcome and the treatment assignment mechanism. Matching is often based on a set of time invariant covariates together with one or a few pre-treatment measurements of the outcome. This paper proposes estimation strategies using a large number of pre-treatment measurements of the outcome to consistently estimate the average treatment effect of the treated (ATET). The assumptions under which these approaches are valid are given. It is shown when and how the strategies can be used to replace, or add to, time-invariant covariates to identify and consistently estimate the ATET. The theoretical results and estimation strategies are illustrated by a study of electricity consumption.

Place, publisher, year, edition, pages
2018. , p. 34
Series
Working paper / Department of Statistics, Uppsala University ; 2018:1
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-355260OAI: oai:DiVA.org:uu-355260DiVA, id: diva2:1228196
Note

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

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

Available from: 2018-06-27 Created: 2018-06-27 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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Schultzberg, Mårten

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