https://mneubrander.github.io/Recent content in About on Marie NeubranderHugoen-us© 2025 Marie NeubranderTue, 01 Apr 2025 00:00:00 +0000https://mneubrander.github.io/research/project-1/Tue, 01 Apr 2025 00:00:00 +0000https://mneubrander.github.io/research/project-1/Estimating causal effects of textual properties is challenging because adjustment representations learned from the full text can directly encode the treatment, creating overlap violations even when the underlying causal problem is well-posed. We propose masking-based adjustment representations that remove treatment-defining lexical signals before representation learning, and we formalize when and why this preserves overlap. Across simulations, masking improves overlap, stabilizes effect estimates, and reduces bias compared to methods that learn from unmasked text.https://mneubrander.github.io/research/project-2/Tue, 15 Oct 2024 00:00:00 +0000https://mneubrander.github.io/research/project-2/Testing interventions to promote prosocial behavior on social media has been difficult because researchers lack control over commercial platform features. We address this with a randomized controlled trial on a custom research platform that uses AI bots to simulate social media dynamics, exposing 2,282 U.S. adults to curiosity-priming interventions through modified norms, interface affordances, or both. Curiosity priming increased question-asking and reduced toxicity without harming user experience, suggesting that platform designs prioritizing curiosity can foster prosocial behavior.https://mneubrander.github.io/research/project-5/Mon, 20 May 2024 00:00:00 +0000https://mneubrander.github.io/research/project-5/This work develops efficient algorithms—a frequency-based maximum-ID method and a linear algebra-based RREF method—for detecting multi-set overlaps and differences in privacy-centric advertising environments, with theoretical guarantees on catching privacy violations and experimental results highlighting additional use cases.https://mneubrander.github.io/research/project-3/Mon, 20 May 2024 00:00:00 +0000https://mneubrander.github.io/research/project-3/Standard multiple imputation methods can produce biased estimates when data are missing not at random, since the missingness mechanism depends on the unobserved values themselves. We develop a Bayesian multiple imputation approach that incorporates known population margins for categorical variables as auxiliary information, helping to anchor imputations even under such non-ignorable missingness. Implemented in JAGS, the method demonstrates reduced bias and improved coverage probability compared to standard MICE procedures across a range of missing data mechanisms.https://mneubrander.github.io/research/project-4/Mon, 20 May 2024 00:00:00 +0000https://mneubrander.github.io/research/project-4/This work uses tensor representations to address the curse of dimensionality in solving the chemical master equation for biochemical reaction systems, and establishes the differences and similarities between two prominent modeling methods through computational examples and a mathematical proof/https://mneubrander.github.io/miscellaneous/fulbright/Sat, 01 Jun 2019 00:00:00 +0000https://mneubrander.github.io/miscellaneous/fulbright/After undergrad, I spent a year teaching English in elementary schools on Kinmen — a small island off the coast of mainland China — through the Fulbright U.S. Student Program. Outside the classroom, I explored the island’s history and culture, became obsessed with night markets, and developed a lifelong love of passionfruit bubble tea.