Causal Inference Methods to Evaluate Health Policies With Spillover Gary Clayton Nolan Hettinger

Format:

Book

Thesis/Dissertation

Author/Creator:

Hettinger, Gary Clayton Nolan, author.

Contributor:

University of Pennsylvania. Epidemiology and Biostatistics., degree granting institution.

Language:

English

Subjects (All):

Biostatistics.

Public health education.

0308.

0500.

0769.

Local Subjects:

Biostatistics.

Public health education.

0308.

0500.

0769.

Physical Description:

1 electronic resource (131 pages)

Contained In:

Dissertations Abstracts International 86-12B

Place of Publication:

Ann Arbor : ProQuest Dissertations and Theses, 2025

Language Note:

English

Summary:

Comprehensively evaluating health policies requires extending beyond simple treated versus untreated comparisons. Many policies generate spillover effects that can amplify or offset intended outcomes, while exposure levels vary among individuals within implementing regions. Understanding these variations is crucial for designing effective interventions. Relatedly, identifying the drivers of policy effect heterogeneity is key to uncovering the mechanisms through which policies operate. These analyses are further complicated by several statistical challenges, including confounding from non-randomized policy implementation in real-world settings, violations of standard causal inference assumptions such as interference between units, and spatial and temporal correlations, which standard methods often fail to address. This dissertation develops novel causal inference methodologies to improve policy evaluations using observational data. First, we introduce a doubly robust difference-in-differences (DiD) framework to estimate policy effects in both implementing and indirectly affected neighboring regions while accounting for spatial interference and relaxing assumptions about confounding. Second, we propose multiply robust estimators for causal effect curves of continuous policy exposures, addressing confounding between intervention status, exposure levels, and outcome trends while avoiding strong parametric assumptions. Third, we construct a causal framework for evaluating policy effect heterogeneity by representing relevant research questions as hypothetical interventions and proposing robust estimation approaches, enabling researchers to assess pathways driving effect heterogeneity while addressing confounding and neighborhood dynamics. We evaluate these methods through extensive simulations and apply them to study Philadelphia's sweetened beverage tax. Our findings highlight the importance of accounting for spillover effects and heterogeneity in policy exposure and effects when designing and assessing public health policies

Notes:

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

Advisors: Mitra, Nandita Committee members: Lee, Youjin; Linn, Kristin A.; Roy, Jason A.; Small, Dylan S.

Ph.D. University of Pennsylvania 2025

Local Notes:

School code: 0175

ISBN:

9798280762589

Access Restriction:

Restricted for use by site license