Working Papers
- "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) [Minor revision, Political Analysis] [arXiv] [software]
Abstract
Many inferential tasks require estimating uncertainty for predictions at points not present in the observed data. Conventional methods typically select and fit a single model to the observed data, resulting in uncertainty estimates that neglect model uncertainty. This is particularly problematic as predictions extend into data-sparse regions. We consider the Gaussian Process (GP) as an appealing solution. While it fits models in a large and flexible function space (associated with the choice of kernel), it provides a posterior distribution for predicted values given that space of models and the observed data. The resulting estimates encompass model uncertainty lost to other approaches, appropriately reflecting increased uncertainty over predictions in regions with little or no data. We provide an accessible introduction to GPs with a focus on this property and introduce a simple, fully automated approach to hyperparameter selection—often a barrier to practical use—implemented in the R package gpss. We illustrate the use of GPs in capturing counterfactual uncertainty across three settings: (i) treatment effect estimation with poor covariate overlap between treated and control groups; (ii) interrupted time series designs requiring extrapolation beyond observed pre-intervention data; and (iii) regression discontinuity designs, where estimates hinge on behavior at the boundary of the data.
- "Non-existent outcomes in research on inequality: A causal approach." (with Ian Lundberg) [Under review, Sociological Methods & Research] [arXiv] [software]
Abstract
Scholars of social stratification often study exposures that shape life outcomes. But some outcomes (such as wage) only exist for some people (such as those who are employed). We show how a common practice—dropping cases with non-existent outcomes—can obscure causal effects when a treatment affects both outcome existence and outcome values. 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 effects of parenthood on labor market outcomes.
- "Not quite refugees?: Examining skepticism toward climate-displaced populations." (with Jieun Park and Margaret Peters)
Abstract
Climate-induced migration is a pressing global issue, with millions projected to face displacement, yet public attitudes toward climate refugees remain understudied. Existing research treats refugees as monolithic or exclusively war-driven, obscuring how displacement causes shape public perceptions. We argue that climate refugees face systematic disadvantage relative to war refugees due to heightened suspicion about their economic motivations—a skepticism particularly pronounced in developed countries where robust disaster management systems create expectations that climate impacts should be handled domestically. Drawing on cross-national survey data across 28 countries and two original survey experiments in the United States, we test how motivational suspicion shapes refugee preferences. We first find that climate refugee disadvantage concentrates in developed democracies, where suspicion about economic motivations predicts this support gap. Experimentally priming such suspicion disproportionately erodes climate refugee support. Second, displacement cause generates stronger attitudinal differences than regional origin, with climate refugees facing skepticism about displacement legitimacy regardless of demographics. These results extend group-specific attitude theories to climate-displaced populations and suggest that building public support requires addressing legitimacy concerns beyond humanitarian appeals.
- "Causal inference without controls: Gaussian Processes for estimating treatment effects in Interrupted Time Series." [Job Market Paper]
Research in Progress
- "The effects of COVID-19 Stay-at-home orders on gun purchasing." (with Jack Kappelman, Tanvi Shinkre, Ryan Baxter-King, Haotian (Barney) Chen)
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.
- "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)