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Outcome regression methods in causal inference: The difference LASSO and selection of effect modifiers
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
2018 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesisAlternative title
Regressionsmetoder for utfall inom kausal inferens : "The difference LASSO" och val av effektmodifierare (Swedish)
Abstract [en]

In causal inference, a central aim of covariate selection is to provide a subset of covariates, that is sufficient for confounding adjustment. One approach for this is to construct a subset of covariates associated with the outcome. This is sometimes referred to as the outcome approach, which is the subject for this thesis. Apart from confounding, there may exist effect modification. This occurs when a treatment has different effect on the outcome, among different subgroups, defined by effect modifiers.

We describe how the outcome approach implemented by regression models, can be used for estimating the ATE, and how sufficient subsets of covariates may be constructed for these models. We also describe a novel method, called the difference LASSO, which results in identification of effect modifiers, rather than determination of sufficient subsets. The method is defined by an algorithm where, in the first step, an incorrectly specified model is fitted. We investigate the bias, arising from this misspecification, analytically and numerically for OLS.

The difference LASSO is also compared with a regression estimator. The comparison is done in a simulation study, where the identification of effect modifiers is evaluated. This is done by analyzing the proportion of times a selection procedure results in a set of covariates including only the effect modifiers, or a set where the effect modifiers are included as a subset.

The results show that the difference LASSO works relatively well for identification of effect modifiers. Among four designs, a set containing only the true effect modifiers were selected in at least 83:2%. The corresponding result for the regression estimator was 27:9%. However, the difference LASSO builds on biased estimation. Therefore, the method is not plausible for interpretation of treatment effects.

Place, publisher, year, edition, pages
2018.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-149423OAI: oai:DiVA.org:umu-149423DiVA, id: diva2:1221802
Available from: 2018-06-20 Created: 2018-06-20 Last updated: 2018-06-20Bibliographically approved

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CiteExportLink to record
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