The paper introduces Bayesian Propensity Score Predictive Inference (PSPI), an interaction‑based outcome modeling framework that uses Bayesian additive regression trees and natural cubic splines of the sampling propensity score to extend randomized trial results to broader target populations. PSPI includes variants that apply splines to the treatment effect component alone or to both prognostic and treatment effect components, enabling separate regularization of selection‑related variation and explicit extrapolation beyond boundary knots. Simulation studies demonstrate that spline‑based PSPI models outperform competing methods, achieving lower root mean squared errors and near‑nominal coverage, especially under substantial covariate shift, and in the CODIACS Vanguard trial application PSPI estimates larger intervention benefits than the unadjusted trial estimate.
arXiv:2610.18889v1 Announce Type: cross Abstract: Estimating (heterogeneous) treatment effects is still challenging when the outcome is semicontinuous (i.e., exhibiting both a discrete point mass at...
arXiv:2604. 16763v3 Announce Type: replace Abstract: Causal inference from electronic health records (EHR) is fundamentally limited by unmeasured confounding: critical clinical states such as frailty, goals of care, and mental status are documented in free-text notes but absent from structured data.
arXiv:2607. 26521v1 Announce Type: new Abstract: Conventional subgroup analyses can yield unstable and difficult-to-interpret conclusions, especially in observational biomedical data where each individual is observed under only one exposure state, true individual treatment effects are unavailable, and causal structure is uncertain.
arXiv:2607. 27224v1 Announce Type: cross Abstract: External and synthetic control arms (ECAs) are entering psychiatric drug development, but the field lacks a benchmark that evaluates the properties regulators care about: not only how accurately a method reconstructs untreated trajectories, but whether its uncertainty is calibrated, whether it is robust to the informative observation times common in mental-health records (sicker patients are seen more often), and what false-positive rate it induces in go/no-go trial decisions.
arXiv:2608. 19383v1 Announce Type: cross Abstract: Average dose-response functions are widely used to summarize causal effects of continuous treatments, but most existing methods assume that the observed sample represents the target population.
The paper introduces a new algorithm that uses decision trees and random forests to estimate individual treatment effects while providing interpretability. It modifies the standard random forest splitting criterion by combining a heterogeneity-focused criterion with a bias-correction criterion, enabling the model to handle observational studies with varying treatment propensities without separately estimating propensity scores. The resulting tree structure directly reveals which features drive treatment effect differences, and simulation studies show the method matches or surpasses existing approaches in prediction accuracy while improving interpretability.
The paper introduces Pro-Active Inference (PAI), a method for efficiently estimating a population mean when only a subset of outcomes can be observed. PAI proactively selects which observation to label next from a covariate pool, updating machine‑learning predictions online, and provides finite‑sample unbiasedness, asymptotic validity, and an improved variance estimator. Experiments on eight datasets show that PAI outperforms Sequential Active Inference in most settings, while diagnostics reveal when additional adaptivity is beneficial or detrimental.
arXiv:2601. 20819v2 Announce Type: replace-cross Abstract: Machine learning predictions are increasingly used to supplement incomplete or costly-to-measure outcomes in fields such as biomedical research, environmental science, and social science.
arXiv:2610.34349v1 Announce Type: cross Abstract: In longitudinal studies, repeated treatments may be followed by different sequences of mediators and time-varying covariates. We develop semiparametr...
arXiv:2505. 00571v3 Announce Type: replace-cross Abstract: Machine Learning (ML) is gaining popularity in epidemiology and healthcare studies for hypothesis-free discovery of risk and protective factors.
arXiv:2608. 19501v1 Announce Type: cross Abstract: Diagnostic medical tests and devices provide useful information for evaluating the potential benefits and risks of therapeutic treatments.
FRESH (Fusion of Recent Evidence and Subject Histories) is a method that integrates population‑level summary results—such as published clinical trials, registry summaries, prior natural‑history studies, and peer‑reviewed indirect comparisons—into predictive models trained on patient‑level data. It recalibrates a generative model trained on patient data so that its predictions match specified aggregate statistics for a target population, achieving this with minimal perturbation in an information‑geometric sense. The recalibrated model can be used directly or to update the original generative model, enabling applications like contextualizing single‑arm trials, simulating clinical‑trial designs, and conducting comparative‑effectiveness analyses of on‑market therapies.