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Proxy variables and nonparametric identification of causal effects
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)
2017 (English)In: Economics Letters, ISSN 0165-1765, E-ISSN 1873-7374, Vol. 150, 152-154 p.Article in journal (Refereed) Published
Abstract [en]

Proxy variables are often used in linear regression models with the aim of removing potential confounding bias. In this paper we formalise proxy variables within the potential outcomes framework, giving conditions under which it can be shown that causal effects are nonparametrically identified. We characterise two types of proxy variables and give concrete examples where the proxy conditions introduced may hold by design.

Place, publisher, year, edition, pages
2017. Vol. 150, 152-154 p.
Keyword [en]
Average treatment effect, Observational studies, Potential outcomes, Unobserved confounders
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-129519DOI: 10.1016/j.econlet.2016.11.018ISI: 000392568300038OAI: oai:DiVA.org:umu-129519DiVA: diva2:1061144
Available from: 2016-12-31 Created: 2016-12-31 Last updated: 2017-09-15Bibliographically approved
In thesis
1. Methods for improving covariate balance in observational studies
Open this publication in new window or tab >>Methods for improving covariate balance in observational studies
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Metoder för att förbättra jämförbarheten mellan två grupper i observationsstudier
Abstract [en]

This thesis contributes to the field of causal inference, where the main interest is to estimate the effect of a treatment on some outcome. At its core, causal inference is an exercise in controlling for imbalance (differences) in covariate distributions between the treated and the controls, as such imbalances otherwise can bias estimates of causal effects. Imbalance on observed covariates can be handled through matching, where treated and controls with similar covariate distributions are extracted from a data set and then used to estimate the effect of a treatment.

The first paper of this thesis describes and investigates a matching design, where a data-driven algorithm is used to discretise a covariate before matching. The paper also gives sufficient conditions for if, and how, a covariate can be discretised without introducing bias.

Balance is needed for unobserved covariates too, but is more difficult to achieve and verify. Unobserved covariates are sometimes replaced with correlated counterparts, usually referred to as proxy variables. However, just replacing an unobserved covariate with a correlated one does not guarantee an elimination of, or even reduction of, bias. In the second paper we formalise proxy variables in a causal inference framework and give sufficient conditions for when they lead to nonparametric identification of causal effects.

The third and fourth papers both concern estimating the effect an enhanced cooperation between the Swedish Social Insurance Agency and the Public Employment Service has on reducing sick leave. The third paper is a study protocol, where the matching design used to estimate this effect is described. The matching was then also carried out in the study protocol, before the outcome for the treated was available, ensuring that the matching design was not influenced by any estimated causal effects. The third paper also presents a potential proxy variable for unobserved covariates, that is used as part of the matching. The fourth paper then carries out the analysis described in the third paper, and uses an instrumental variable approach to test for unobserved confounding not captured by the supposed proxy variable.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2017. 29 p.
Series
Statistical studies, ISSN 1100-8989 ; 52
Keyword
causal effect, coarsening, discretisation, proxy variables, register study, swedish social insurance agency, unobserved variables
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-139523 (URN)978-91-7601-751-7 (ISBN)
Public defence
2017-10-10, Hörsal G, Humanisthuset, Umeå, 10:00 (English)
Opponent
Supervisors
Available from: 2017-09-19 Created: 2017-09-15 Last updated: 2017-10-02Bibliographically approved

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Citation style
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