Causal inference methods have been applied in various fields where researchers want to establish causal effects between different phenomena. The goal of causal inference is to estimate treatment effects by comparing outcomes had units received treatment versus outcomes had units not received treatment. We focus on estimating treatment effects in three different projects.
We first proposed linear unbiased estimators (LUEs) for general causal effects under the assumption that treatment effects are additive. Under the assumption of additivity, the set of estimands considered grows as contrasts in exposures are now equivalent. Furthermore, we identified a subset of LUEs that forms an affine basis for LUEs, and we characterized LUEs with minimum integrated variance through defining conditions on the support of the estimator.
We also estimated the effect of drug-induced homicide (DIH) prosecutions reported by the media on unintentional drug overdose deaths, which have never been empirically assessed, using various models. Using a difference-in-differences-like logistic generalized additive model (GAM) with smoothed time effects where we assumed a constant treatment effect, we found that DIH prosecutions reported by the media were associated with a potential harmful effect (risk ratio: 1.064; 95% CI: (1.012, 1.118)) on drug overdose deaths. Upon further research, however, there are potential issues using a constant treatment effect model in a setting where treatment is staggered and treatment effects are heterogeneous. Therefore, we also used a GAM with a linear link function where we assumed that treatment effects may depend on the treatment duration. With this second model, we estimated a risk ratio for having any DIH prosecutions reported by the media of 0.956 (95% CI: (0.824, 1.110)) and a risk ratio of 0.986 (95% CI: (0.973, 0.999)) for the effect of being exposed to DIH prosecutions reported by the media for each additional six months. Despite being statistically significant, the effects were not practically significant. However, the results call for further research on the effect of DIH prosecutions on drug overdose deaths.
Lastly, we shift our focus to Structural Nested Mean Models (SNMMs). We extended SNMMs to a new class of estimators which estimate treatment effects of different treatment regimes in the risk ratio scale---the Structural Nested Risk Ratio Model (SNRRM). We further generalized previous work on SNMMs by estimating treatment effects by modeling a function of treatment, which we choose to be any function that can be modeled by generalized linear models, as opposed to just a model for treatment initiation. We applied SNRRMs to estimate the effect of DIH prosecutions reported by the media on drug overdose deaths.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/46388 |
| Date | 23 June 2023 |
| Creators | Kung, Kelly C. |
| Contributors | Sussman, Daniel L., Lok, Judith J. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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