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Data-driven algorithms for dimension reduction in causal inference
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)
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: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 105, 280-292 p.Article in journal (Refereed) Published
Abstract [en]

In observational studies, the causal effect of a treatment may be confounded with variables that are related to both the treatment and the outcome of interest. In order to identify a causal effect, such studies often rely on the unconfoundedness assumption, i.e., that all confounding variables are observed. The choice of covariates to control for, which is primarily based on subject matter knowledge, may result in a large covariate vector in the attempt to ensure that unconfoundedness holds. However, including redundant covariates can affect bias and efficiency of nonparametric causal effect estimators, e.g., due to the curse of dimensionality. In this paper, data-driven algo- rithms for the selection of sufficient covariate subsets are investigated. Under the assumption of unconfoundedness we search for minimal subsets of the covariate vector. Based on the framework of sufficient dimension reduction or kernel smoothing, the algorithms perform a backward elim- ination procedure testing the significance of each covariate. Their performance is evaluated in simulations and an application using data from the Swedish Childhood Diabetes Register is also presented.

Place, publisher, year, edition, pages
2017. Vol. 105, 280-292 p.
Keyword [en]
covariate selection, marginal co-ordinate hypothesis test, matching, kernel smoothing, type 1 diabetes mellitus
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-80696DOI: 10.1016/j.csda.2016.08.012ISI: 000385604500019OAI: oai:DiVA.org:umu-80696DiVA: diva2:651007
Funder
Swedish National Infrastructure for Computing (SNIC), SNIC 2016/1-2Swedish Research Council, 2013-672Swedish Research Council, 07531Riksbankens Jubileumsfond, P11-0814:1
Available from: 2013-09-24 Created: 2013-09-24 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Causal inference and case-control studies with applications related to childhood diabetes
Open this publication in new window or tab >>Causal inference and case-control studies with applications related to childhood diabetes
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Kausal inferens och fall-kontroll studier med applikationer inom barndiabetes
Abstract [en]

This thesis contributes to the research area of causal inference, where estimation of the effect of a treatment on an outcome of interest is the main objective. Some aspects of the estimation of average causal effects in observational studies in general, and case-control studies in particular, are explored.

An important part of estimating causal effects in an observational study is to control for covariates. The first paper of this thesis concerns the selection of minimal covariate sets sufficient for unconfoundedness of the treatment assignment. A data-driven implementation of two covariate selection algorithms is proposed and evaluated.

A common sampling scheme in epidemiology, and when investigating rare events, is the case-control design. In the second paper we study estimators of the marginal causal odds ratio in matched and independent case-control designs. Estimators that, under a logistic regression model, utilize information about the known prevalence of being a case is examined and compared through simulations.

The third paper investigates the particular situation where case-control sampled data is reused to estimate the effect of the case-defining event on an outcome of interest. The consequence of ignoring the design when estimating the average causal effect is discussed and a design-weighted matching estimator is proposed. The performance of the estimator is evaluated with simulation experiments, when matching on the covariates directly and when matching on the propensity score.

The last paper studies the effect of type 1 diabetes mellitus (T1DM) on school achievements using data from the Swedish Childhood Diabetes Register, a population-based incidence register. We apply theoretical results from the second and third papers in the estimation of the average causal effect within the T1DM population. A matching estimator that accounts for the matched case-control design is used.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2014. 22 p.
Series
Statistical studies, ISSN 1100-8989 ; 48
Keyword
covariate selection, design-weighted estimation, marginal effect, matching, register study, treatment effect, type 1 diabetes mellitus
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-94993 (URN)978-91-7601-151-5 (ISBN)
Public defence
2014-11-21, Hörsal F, Humanisthuset, Umeå universitet, Umeå, 10:15 (English)
Opponent
Supervisors
Available from: 2014-10-24 Created: 2014-10-20 Last updated: 2014-10-23Bibliographically approved

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Available from 2019-01-01 00:00

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