Search Results for author: Walter Dempsey

Found 7 papers, 3 papers with code

Assessing Time-Varying Causal Effect Moderation in the Presence of Cluster-Level Treatment Effect Heterogeneity

2 code implementations2 Feb 2021 Jieru Shi, Zhenke Wu, Walter Dempsey

The micro-randomized trial (MRT) is a sequential randomized experimental design to empirically evaluate the effectiveness of mobile health (mHealth) intervention components that may be delivered at hundreds or thousands of decision points.

Methodology

Kernel Deformed Exponential Families for Sparse Continuous Attention

no code implementations1 Nov 2021 Alexander Moreno, Supriya Nagesh, Zhenke Wu, Walter Dempsey, James M. Rehg

Theoretically, we show new existence results for both kernel exponential and deformed exponential families, and that the deformed case has similar approximation capabilities to kernel exponential families.

A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderation

1 code implementation28 Jun 2023 Jieru Shi, Walter Dempsey

Twin revolutions in wearable technologies and smartphone-delivered digital health interventions have significantly expanded the accessibility and uptake of mobile health (mHealth) interventions across various health science domains.

Meta-Learning

Non-Stationary Latent Auto-Regressive Bandits

1 code implementation5 Feb 2024 Anna L. Trella, Walter Dempsey, Finale Doshi-Velez, Susan A. Murphy

We consider the stochastic multi-armed bandit problem with non-stationary rewards.

It’s quality and quantity: the effect of the amount of comments on online suicidal posts

no code implementations EMNLP (CINLP) 2021 Daniel Low, Kelly Zuromski, Daniel Kessler, Satrajit S. Ghosh, Matthew K. Nock, Walter Dempsey

We use propensity score stratification, a causal inference method for observational data, and estimate whether the amount of comments —as a measure of social support— increases or decreases the likelihood of posting again on SW. One hypothesis is that receiving more comments may decrease the likelihood of the user posting in SW in the future, either by reducing symptoms or because comments from untrained peers may be harmful.

Causal Inference

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