arXiv subjects

Amelia Haviland

Publications and source records attributed to Amelia Haviland.

3 recordsLinked to original sources

arXiv2023

The effect of COVID-19 vaccinations on self-reported depression and anxiety during February 2021

Using the COVID-19 Trends and Impact survey, we find that COVID-19 vaccinations reduced the prevalence of self-reported feelings of depression and anxiety, isolation, and worries about health among vaccine-accepting respondents in February 2021 by 3.7, 3.3, and 4.3 percentage points, respectively, with particularly large reductions among respondents aged 18 and 24 years old. We show that interventions targeting social isolation account for 39.1\% of the total effect of COVID-19 vaccinations on depression, while interventions targeting worries about health can account for 8.3\%. This suggests that social isolation is a stronger mediator of the effect of COVID-19 vaccinations on depression than worries about health. We caution that these causal interpretations rely on strong assumptions.

stat.AP↗

arXiv2022

Balancing weights for region-level analysis: the effect of Medicaid Expansion on the uninsurance rate among states that did not expand Medicaid

We predict the average effect of Medicaid expansion on the non-elderly adult uninsurance rate among states that did not expand Medicaid in 2014 as if they had expanded their Medicaid eligibility requirements. Using American Community Survey data aggregated to the region level, we estimate this effect by finding weights that approximately reweights the expansion regions to match the covariate distribution of the non-expansion regions. Existing methods to estimate balancing weights often assume that the covariates are measured without error and do not account for dependencies in the outcome model. Our covariates have random noise that is uncorrelated with the outcome errors and our outcome model has state-level random effects inducing dependence between regions. To correct for the bias induced by the measurement error, we propose generating our weights on a linear approximation to the true covariates, using an idea from measurement error literature known as "regression-calibration" (see, e.g., Carroll (2006)). This requires auxiliary data to estimate the variability of the measurement error. We also modify the Stable Balancing Weights objective proposed by Zubizaretta (2015)) to reduce the variance of our estimator when the model errors follow our assumed correlation structure. We show that these approaches outperform existing methods when attempting to predict observed outcomes during the pre-treatment period. Using this method we estimate that Medicaid expansion would have caused a -2.33 (-3.54, -1.11) percentage point change in the adult uninsurance rate among states that did not expand Medicaid.

stat.AP↗

arXiv2020

Learning and Testing Sub-groups with Heterogeneous Treatment Effects:A Sequence of Two Studies

There is strong interest in estimating how the magnitude of treatment effects of an intervention vary across sub-groups of the population of interest. In our paper, we propose a two-study approach to first propose and then test heterogeneous treatment effects. In Study 1, we use a large observational dataset to learn sub-groups with the most distinctive treatment-outcome relationships ('high/low-impact sub-groups'). We adopt a model-based recursive partitioning approach to propose the high/low impact sub-groups, and validate them by using sample-splitting. While the first study rules out noise, there is potential bias in our estimated heterogeneous treatment effects. Study 2 uses an experimental design, and here we classify our sample units based on sub-groups learned in Study 1. We then estimate treatment effects within each of the groups, thereby testing the causal hypotheses proposed in Study 1. Using patient claims data from the NBER MarketScan database, we apply our approach to estimate heterogeneous effects of a switch to a high-deductible health insurance plan on use of outpatient care by patients with a common chronic condition. We extend the method to non-parametrically learn the sub-groups in Study 1. We also compare the methods' performance to other state-of-the-art methods in the literature that make use only of the Study 2 data.

stat.ME↗