continuous-treatment did

This paper addresses the challenge of modeling causal dose–response relationships between time-varying continuous exposures (e.g., dynamic dosing trajectories) and longitudinal outcomes. We propose the first scalable nonparametric Bayesian framework for this purpose. Methodologically, it innovatively integrates a two-level nonparametric generalized Bayesian bootstrap with generalized estimating equations (GEE), incorporates generalized propensity score modeling and inverse probability weighting, and employs a Dirichlet process prior to flexibly characterize the exposure–effect function—without assuming a prespecified functional form—while accommodating temporal dependence and dynamic confounding. The framework enables causal effect estimation at arbitrary exposure levels. Empirically, applied to panel data on monthly metro ridership and COVID-19 case growth across multiple cities, it identifies a statistically significant positive causal dose–response relationship between ridership increases and accelerated case growth, demonstrating both methodological validity and practical utility.

This study addresses bias in dose–response estimation in randomized trials arising from measurement error in drug exposure or unobserved confounding. We propose a robust causal inference method leveraging dose itself as a natural instrumental variable (IV), integrating control-function and ANCOVA-based adjustment. Unlike conventional approaches, our method does not require correct specification of the exposure–outcome model and remains consistent and asymptotically normal even under model misspecification. Theoretical analysis establishes its robustness to unobserved confounding, while simulations demonstrate excellent finite-sample performance. Applied to a CAR-T cell therapy clinical trial, it significantly improves accuracy in identifying the optimal dose. Our key contribution is the first formalization of dose as a built-in IV, enabling a model-agnostic, misspecification-robust framework for causal dose–response estimation—balancing interpretability with statistical rigor.

This study addresses the challenge of integrating randomized or single-arm clinical trials with external experimental or observational data to enable cross-study treatment comparisons and improve estimation precision of treatment effects. Methodologically, building upon the potential outcomes framework, we first develop a unified identification strategy for hybrid-data designs, systematically characterizing identifiability conditions across diverse designs—including historical controls, synthetic controls, and anchoring estimators—and propose a generalizable taxonomy of such designs along with corresponding causal inference principles. Our contribution lies in filling a critical theoretical gap in regulatory science regarding the rigorous integration of external controls, thereby establishing a methodological foundation for leveraging real-world evidence to complement trial-based evidence in pharmaceutical and medical device evaluation. This advancement significantly enhances the transportability of evidence and its applicability to regulatory decision-making.

This paper addresses the challenge of estimating heterogeneous dose–response curves (HDRCs) under long-term continuous treatment, where existing methods rely on strong assumptions—such as no unmeasured confounding and binary treatment—that hinder personalized decision-making. We propose an optimal transport-based weighting framework for data alignment, the first to incorporate optimal transport into long-term causal inference; it mitigates bias from unmeasured confounding via reweighting. We derive a generalization bound for counterfactual prediction under the reweighted distribution and jointly model continuous dosing and individual-level heterogeneous treatment effects. On synthetic and semi-synthetic benchmarks, our HDRC estimator reduces estimation error by over 30% compared to state-of-the-art methods, demonstrating both the tightness of our theoretical bound and the robustness of the estimator.

This study addresses causal dose–response function estimation under continuous treatment in the presence of unobserved confounding, assuming only a proxy variable for the latent confounder is observed. It proposes the first proximal doubly robust estimator by constructing a novel proximal doubly robust pseudo-outcome, which, combined with local linear regression, quadratic bias correction, and cross-fitting, yields debiased estimates at the mean squared error–optimal bandwidth—without requiring undersmoothing or entropy conditions on function classes. Theoretically, the estimator achieves pointwise and finite-dimensional asymptotic normality as well as uniform Gaussian approximation. Empirical results demonstrate its strong performance in settings with latent confounding.

This study addresses the limitations of traditional control-based causal inference methods—such as matching and difference-in-differences—in settings characterized by pervasive or structurally ambiguous spillover effects, where reliance on uncontaminated control units impedes accurate identification of both average direct and spillover effects. Within the potential outcomes framework, this work provides the first systematic comparison between control-based and prediction-based counterfactual approaches—including interrupted time series and machine learning control—in terms of their identification capabilities. Through simulation and empirical analyses, the authors demonstrate that in environments with widespread interference, prediction-based methods can more reliably estimate certain causal parameters over short horizons, circumventing the stringent assumption of unperturbed units and thereby offering a promising alternative for causal inference under complex interference.

This study addresses treatment switching in oncology randomized controlled trials, a phenomenon that violates randomization and introduces bias in overall survival estimation. To mitigate this issue, the authors propose a weighted causal inference framework that innovatively integrates external controls, synthetic controls, and balancing weights from observational studies. By incorporating multiple imputation and time-varying weights, the method effectively adjusts for the impact of treatment switching on efficacy estimates. The approach avoids strong parametric assumptions and complex modeling structures, demonstrating superior performance over conventional adjustment strategies in simulation studies. Its robustness and practical utility are further validated through applications to two phase III oncology clinical trials.