Research in Progress

  • "Inference at the data's edge: Gaussian processes for estimation and inference in the face of model uncertainty, poor overlap, and extrapolation." (with Chad Hazlett and Doeun Kim) [R&R] [draft] [software]
    • Abstract Many inferential tasks require estimating uncertainty around predictions of an outcome at points not in the data. Conventional approaches choose and fit a model on the observed data then compute uncertainty estimates using that fit, producing uncertainty estimates that neglect the increasing impact of model uncertainty further from observed datapoints. After describing this problem, we consider the Gaussian Process (GP) as an attractive solution, as it estimates (in closed-form) a posterior distribution for the predicted values at test points of interest that automatically combines information about model fit and how far the points in question are from observations. We offer an accessible explanation of GPs emphasizing this feature, and develop a simpler, fully-automated approach to handling the hyperparameters that have proven a source of difficulty in prior iplementations (implemented in the \texttt{R} package \texttt{gpss}). We then illustrate how GPs aid in capturing counterfactual uncertainty in three common settings where model dependency leads to inferential risks: (i) comparisons in which treated and control groups have poor covariate overlap; (ii) interrupted time series (ITS) designs, where models fitted prior to an event are extrapolated to later time points; and (iii) regression discontinuity, where inferences depend on point and uncertainty estimates at the very edges of their supporting data.
  • "Non-existent outcomes in research on inequality: A causal approach." (with Ian Lundberg) [software]
    • Abstract Scholars of social stratification often study exposures that shape life outcomes. Job training may increase hourly wages, for example. But those same exposures may also determine whether outcomes exist at all: job training may help someone find employment so that they have an hourly wage when they would not otherwise. We show how a common research practice---dropping cases with non-existent outcomes---can obscure causal effects when the existence of the outcome is selective, in the sense that it is caused by the exposure of interest. We show how the effects of both beneficial and harmful treatments can be underestimated. Drawing on existing approaches for principal stratification, we show how to study (1) the average effect on whether an outcome exists and (2) the average effect on the outcome among the latent subgroup whose outcome would exist in either treatment condition. To extend our approach to the selection-on-observables settings common in applied research, we develop a framework involving regression and simulation to enable principal stratification estimates that adjust for measured confounders. We illustrate through an empirical example about the effect of motherhood on women's labor market outcomes.
  • "Estimating dynamic treatment effects of events: An imputation approach to Interrupted Time Series design."
  • "Effects of the COVID-19 pandemic and governmental restrictions on firearms purchasing behavior." (with Jack Kappelman, Tanvi Shinkre, Ryan Baxter-King, Haotian Chen, and Abigail Kappelman)
    • Abstract How did the onset of the COVID-19 pandemic in the United States affect gun purchasing behavior? This paper explores the significant increase in firearms purchasing rates triggered by the pandemic and subsequent governmental restrictions to curb the virus's spread. Extant scholarship suggests that distrust in government contributed to record-breaking firearms sales, but this study uses a causal, interrupted time series design with administrative data from the National Instant Criminal Background Check System (NICS) and state-issued public health orders to provide more robust, precisely estimated effects and improved counterfactual projections. We isolate the effect of pandemic-era lockdown orders on gun purchases though our stability-controlled quasi-experimental approach, which accounts for confounding factors, data structure issues, and addresses heterogeneity in the timing of public health orders. Taken together, these results demonstrate that credible causal conclusions can be drawn even amid potential confounding. These findings deepen our understanding of the socio-political dynamics of firearm purchasing in crisis contexts and highlight the impact of government restrictions on consumer behavior, with additional findings speaking to the downstream effects this increase in gun purchasing had on firearms-involved mortality and other health outcomes.
  • "Unraveling climate refugee-specific hostility: Exploring deservingness bias in developed countries." (with Jieun Park and Margaret Peters)
    • Abstract Climate-induced migration is a pressing global issue, with millions projected to be displaced. Understanding public attitudes towards climate refugees is crucial but lacks research focus, often treating refugees as a homogeneous group. This study investigates attitudes towards climate and war refugees, focusing on the underlying factors driving climate refugee-specific hostility. Introducing the concept of deservingness bias, we argue that climate refugee-specific hostility may stem from the suspicion that those fleeing from natural disasters and climate change are not genuine asylum seekers but economic migrants in disguise. This suspicion is notably pronounced in developed economies: people who are more suspicious about the motives of climate refugees support less climate-induced refugees than war-induced ones in developed economies, but not in other countries. We posit that the presence of well-established systems for addressing domestically displaced populations fosters skepticism regarding the motives of climate refugees, potentially leading to biased attitudes. Drawing on data from original survey experiments and Ipsos Survey 2022, this research aims to gain insights into factors shaping attitudes towards climate and war refugees, with a particular focus on deservingness bias. Our findings contribute to comprehending the group-specific attitudes hypothesis and public attitudes toward refugees in a broader context. It also informs policies to promote integration and reduce tensions in the face of climate-driven displacement.
  • "Drivers of attitudes toward Asian Americans: Disentangling stereotyping from racial identity." (with Jieun Park and Jessica Hyunjeong Lee)
    • Abstract Despite substantial research on attitudes toward Asian Americans, we know less about the underlying motivations. Prior studies have demonstrated that individuals make inferences about other characteristics based on someone’s race, hence feelings toward Asian Americans could be a consequence of the correlation between race and other perceived traits. To what extent are attitudes toward Asian Americans driven by dislike for racial outgroup per se, xenophobia, fear of group competition, or other presumed stereotypes? Leveraging original survey experiments that manipulate race signaled by name, immigration status, perceived threats, and stereotyped traits in short vignettes, we aim to disentangle the effects of race and other related traits inferred from it on interpersonal attitudes. Distinguishing among these competing motivations is crucial not only for understanding how perceptions of Asian Americans are shaped but also for understanding the disparate implications of interracial relationships. This study will shed light on what is the principal driver of interpersonal affect regarding race.
  • "Modeling and assessing controlled direct effects with the regression-with-residuals method." (with Ian Lundberg)