arXiv:2509.07013v3 Announce Type: replace
Abstract: Agent-based models (ABMs) are widely used to study infectious disease dynamics, but their calibration is often computationally intensive, limiting their applicability in time-sensitive public health …
arXiv:2604.01946v1 Announce Type: new
Abstract: Estimating optimal individualized treatment rules (ITRs) via outcome weighted learning (OWL) often relies on observed rewards that are noisy or optimistic proxies for the true latent utility. Ignoring th…
arXiv:2604.01339v1 Announce Type: new
Abstract: Vision transformers (ViT) rely on attention mechanism to weigh input features, and therefore attention scores have naturally been considered as explanations for its decision-making process. However, atte…
arXiv:2412.10683v3 Announce Type: replace-cross
Abstract: Parametric Bayesian modeling offers a powerful and flexible toolbox for machine learning. Yet the model, however detailed, may still be wrong, and this can make inferences untrustworthy. In thi…
arXiv:2604.01501v1 Announce Type: cross
Abstract: Causal mediation analysis provides techniques for defining and estimating effects that may be endowed with mechanistic interpretations. With many scientific investigations seeking to address mechanisti…
arXiv:2406.08097v2 Announce Type: replace
Abstract: Nonlinear dimensional reduction with the manifold assumption, often called manifold learning, has proven its usefulness in a wide range of high-dimensional data analysis. The significant impact of t-…
arXiv:2509.07013v2 Announce Type: replace
Abstract: Calibrating agent-based epidemic models is computationally demanding. We present a supervised machine learning calibrator that learns the inverse mapping from epidemic time series to SIR parameters. …
arXiv:2604.00811v1 Announce Type: new
Abstract: Overlap, also known as positivity, is a key condition for causal treatment effect estimation. Many popular estimators suffer from high variance and become brittle when features differ strongly across tre…
arXiv:2405.03083v4 Announce Type: replace-cross
Abstract: Causal effects are often characterized with population summaries. These might provide an incomplete picture when there are heterogeneous treatment effects across subgroups. Since the subgroup s…
arXiv:2406.15904v2 Announce Type: replace-cross
Abstract: Practitioners often face the challenge of deploying prediction models in new environments with shifted distributions of covariates and responses. With observational data, such shifts are often …