Causal Inference in Observational Studies with Complex Design: Multiple Arms, Complex Sampling and Intervention Effects

Causal Inference in Observational Studies with Complex Design: Multiple Arms, Complex Sampling and Intervention Effects

Bibliographic Details
Main Author: Nattino, Giovanni
Language:English
Published: The Ohio State University / OhioLINK 2019
Subjects:
Biostatistics
Causal inference
Propensity score
Matching design
Survey data
Intervention effect
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=osu1562582646286934
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-osu15625826462869342021-08-03T07:11:43Z Causal Inference in Observational Studies with Complex Design: Multiple Arms, Complex Sampling and Intervention Effects Nattino, Giovanni Biostatistics Causal inference Propensity score Matching design Survey data Intervention effect Observational studies are major data sources to infer causal relationships. When using observational data to estimate causal effects, researchers must consider appropriate statistical methodology to account for the non-random allocation of the units to the treatment groups. Such methodology is well-established when the research question involves two treatment groups and results do not need to be generalized to the population from which the study sample has been selected. Relatively few studies have focused on research questions that do not fit into this framework. The goal of this work is to introduce statistical methods to perform causal inference in complex designs. First, I introduce a matching design for estimating treatment effects in the presence of multiple treatment groups. I devise a novel matching algorithm, generating samples that are well-balanced with respect to pre-treatment variables, and discuss the post-matching statistical analyses. Second, I focus on the generalization of causal effects to the population level, specifically when the sample selection is based on complex survey designs. I discuss the extension of the propensity score methodology to survey data, describe a weighted estimator for the common two-stage cluster sample and study its asymptotic properties. Third, I consider the estimation of population intervention effects, which evaluate the impact of realistic changes in the distribution of the treatment in a cohort. I describe estimators for upper and lower bounds of effects of this type, highlighting the implications for policy makers. For each of these three areas of causal inference, I use Monte Carlo simulations to assess the reliability of the proposed methods and compare them with competing approaches. The new methods are illustrated with real-data applications. Finally, I discuss limitations and aspects requiring further work. 2019-10-02 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1562582646286934 http://rave.ohiolink.edu/etdc/view?acc_num=osu1562582646286934 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Biostatistics
Causal inference
Propensity score
Matching design
Survey data
Intervention effect
spellingShingle Biostatistics
Causal inference
Propensity score
Matching design
Survey data
Intervention effect
Nattino, Giovanni
Causal Inference in Observational Studies with Complex Design: Multiple Arms, Complex Sampling and Intervention Effects
author Nattino, Giovanni
author_facet Nattino, Giovanni
author_sort Nattino, Giovanni
title Causal Inference in Observational Studies with Complex Design: Multiple Arms, Complex Sampling and Intervention Effects
title_short Causal Inference in Observational Studies with Complex Design: Multiple Arms, Complex Sampling and Intervention Effects
title_full Causal Inference in Observational Studies with Complex Design: Multiple Arms, Complex Sampling and Intervention Effects
title_fullStr Causal Inference in Observational Studies with Complex Design: Multiple Arms, Complex Sampling and Intervention Effects
title_full_unstemmed Causal Inference in Observational Studies with Complex Design: Multiple Arms, Complex Sampling and Intervention Effects
title_sort causal inference in observational studies with complex design: multiple arms, complex sampling and intervention effects
publisher The Ohio State University / OhioLINK
publishDate 2019
url http://rave.ohiolink.edu/etdc/view?acc_num=osu1562582646286934
work_keys_str_mv AT nattinogiovanni causalinferenceinobservationalstudieswithcomplexdesignmultiplearmscomplexsamplingandinterventioneffects
_version_ 1719455827578847232

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