arXiv subjects

Kaitlyn Lee

Publications and source records attributed to Kaitlyn Lee.

3 recordsLinked to original sources

arXiv2026

Improving Variance Estimation for Covariate Adjustment with Binary Outcomes

Covariate adjustment is a general method for improving precision when estimating treatment effects in randomized trials and is recommended by the FDA in its 2023 guidance when baseline variables are prognostic for the primary outcome. We focus on a method highlighted in that guidance called ``standardization" (or ``g-computation") for estimating the marginal treatment effect. We address the question of how to reliably estimate variance for binary outcomes when marginal outcome probabilities are close to 0 or 1. We propose an influence function-based leave-one-out cross-validated (IF-LOO) variance estimator for the standardized difference-in-means average treatment effect. Through simulation studies, we show that this estimator provides appropriate type-I error control and performs reliably in challenging settings where existing methods can yield inflated type-I error or fail entirely, such as when outcome events are rare or sample sizes are small. In addition to having desirable statistical properties, we derive a closed-form expression for the proposed estimator, enabling straightforward and reliable implementation by study statisticians. The robust finite-sample performance and ease of implementation suggest the IF-LOO variance estimator is a prudent default choice for standardization in clinical trials.

stat.ME↗

arXiv2025

Targeted Deep Architectures: A TMLE-Based Framework for Robust Causal Inference in Neural Networks

Modern deep neural networks are powerful predictive tools yet often lack valid inference for causal parameters, such as treatment effects or entire survival curves. While frameworks like Double Machine Learning (DML) and Targeted Maximum Likelihood Estimation (TMLE) can debias machine-learning fits, existing neural implementations either rely on "targeted losses" that do not guarantee solving the efficient influence function equation or computationally expensive post-hoc "fluctuations" for multi-parameter settings. We propose Targeted Deep Architectures (TDA), a new framework that embeds TMLE directly into the network's parameter space with no restrictions on the backbone architecture. Specifically, TDA partitions model parameters - freezing all but a small "targeting" subset - and iteratively updates them along a targeting gradient, derived from projecting the influence functions onto the span of the gradients of the loss with respect to weights. This procedure yields plug-in estimates that remove first-order bias and produce asymptotically valid confidence intervals. Crucially, TDA easily extends to multi-dimensional causal estimands (e.g., entire survival curves) by merging separate targeting gradients into a single universal targeting update. Theoretically, TDA inherits classical TMLE properties, including double robustness and semiparametric efficiency. Empirically, on the benchmark IHDP dataset (average treatment effects) and simulated survival data with informative censoring, TDA reduces bias and improves coverage relative to both standard neural-network estimators and prior post-hoc approaches. In doing so, TDA establishes a direct, scalable pathway toward rigorous causal inference within modern deep architectures for complex multi-parameter targets.

cs.LG↗

arXiv2021

Redycler: Daily Outfit Texture Fabrication Appliance Using Re-Programmable Dyes

We present a speculative design for a novel appliance for future fabrication in the home to revitalize textiles using re-programmable multi-color textures. Utilizing colored photochromic dyes activated by ultraviolet (UV) light, we can selectively deactivate hues using complementary colors in visible light to result in the final desired dye pattern. Our proposed appliance would automate this process within a box placed in the bedroom. We envision a future where people are able to transform old apparel into unique and fashionable pieces of clothing. We discuss how the user would interact with the appliance and how this device elongates the life-cycle of clothing through modification. We also outline the central issues to integrate such a concept into the home. Finally, we analyze how this device fits into personal modification trends in HCI to show how this device could change existing conceptions around sustainable fashion and personal style.

cs.HC↗