experimental design

This paper addresses the causal distinction between the per-protocol effect (PPE) and the treatment strategy effect (TSE) in randomized trials. Using the potential outcomes framework and causal graph models, we rigorously establish that these two effects differ fundamentally in their causal definitions, identifiability conditions, and data requirements—particularly regarding dependence on assignment information—and are generally non-interchangeable. Even under complete randomization, identifying either effect necessitates explicit use of assignment mechanism information, challenging the conventional belief that randomization automatically eliminates confounding. We further derive necessary and sufficient conditions for PPE–TSE equality and formally refute the common practice in observational studies of substituting TSE for PPE—unless strong additional assumptions hold. These results provide a theoretical foundation and practical guidelines for causal interpretation and analysis of clinical trials.

In resource-constrained multiple testing, exhaustive evaluation of all hypotheses and computation of exact test statistics (e.g., via experiments or precise calculations) is infeasible. Method: This paper proposes a surrogate-driven active testing framework that leverages auxiliary information—such as expert judgment, ML predictions, or historical data—to construct surrogate test statistics. It dynamically decides whether to invoke costly exact tests; otherwise, it substitutes the surrogate values directly. Contribution/Results: The framework is the first to enable compatible p-value and e-value constructions under arbitrary dependence structures—without requiring independence between surrogates and true statistics—while provably controlling the false discovery rate (FDR). By unifying active learning, multiple testing theory, and e-value theory, it achieves both theoretical rigor and practical utility. Empirical evaluation on scCRISPR causal effect analysis demonstrates a 32% increase in discoveries and a 68% reduction in computational cost compared to exhaustive testing, under identical FDR constraints.

This paper identifies a causal inference bias in service-intervention randomized controlled trials (RCTs) arising from provider capacity constraints: limited resources induce cross-participant interference and treatment dose heterogeneity, rendering the average treatment effect dependent on both sample size and capacity, and causing effect attenuation beyond a critical threshold—yielding non-monotonic, inverted-U-shaped statistical power. We formally characterize this capacity-constrained, queue-based interference mechanism for the first time, integrating queuing theory (square-root staffing rule), causal inference, and experimental design. Our method jointly optimizes provider capacity and sample size to maximize power under resource constraints. Results demonstrate substantial gains in statistical power, reduced required resources and participant enrollment, and provide a mechanistic explanation for the common phenomenon of intervention efficacy fading upon scaling—from RCT success to real-world failure.

This study addresses the inconsistency in causal effect estimates between observational studies and randomized controlled trials (RCTs) by proposing the first unified framework for decomposing causal effect heterogeneity. The framework systematically identifies and quantifies three sources of heterogeneity: differences in covariate distributions, variation in mediating pathways, and shifts in outcome-generating mechanisms. Methodologically, it formally defines effect decomposition across data types (observational vs. experimental), integrating causal inference, sensitivity analysis, and decomposition modeling, while enabling robust parameter estimation under multiple hypotheses. Evaluated through simulation studies and an empirical analysis of the “Moving to Opportunity” experiment, the framework demonstrates improved interpretability, robustness, and policy generalizability in synthesizing evidence from heterogeneous data sources.

This paper addresses causal inference in network experiments subject to interference. We propose a purely design-based, model-agnostic weighted least squares framework. Methodologically, we first establish the equivalence between the Hájek estimator and a specific inverse-probability-weighted regression coefficient. Second, we develop a bias-corrected network-robust covariance adjustment that ensures design-based validity of standard errors under arbitrary regression misspecification. Theoretically, our estimator is consistent and asymptotically normal. Simulations and empirical applications demonstrate stable confidence interval coverage exceeding 95%. Our approach balances practical implementability, flexible incorporation of covariates, and design-based robustness—offering a new paradigm for causal inference in network experiments that unifies theoretical rigor with empirical applicability.

This study addresses the inferential bias arising from misspecification of a single causal model under model uncertainty. The authors propose a weighted triangulation framework that integrates identification functionals from multiple candidate causal models through a data-driven measure of model validity, enabling robust causal effect estimation without explicit model selection. This approach uniquely bridges testability in causal discovery with semiparametric inference, formalizing robustness under causal pluralism without requiring consensus among models or reliance on any single specification. Theoretically, the proposed functional is shown to converge to the true causal effect with high probability. Both simulation studies and empirical analyses demonstrate the method’s robustness and effectiveness.

This study addresses the challenge of accurately identifying subregions with genuine treatment effects in multi-site randomized policy experiments, where conventional multiple testing procedures often lack power. The authors propose a top-down, tree-structured sequential testing procedure that begins by evaluating the overall effect and then recursively tests groups of sites and individual sites, halting further testing within any branch once non-significance is encountered. This approach innovatively integrates a hierarchical tree framework, Hommel-type correction, and adaptive α allocation to enhance detection power for heterogeneous effects while rigorously controlling the weak familywise error rate (FWER). In simulations based on a 44-site education experiment, the method achieved a 44% detection rate—approximately four times higher than the standard Hommel procedure (11%)—and was successfully applied across 25 MDRC education trials.

This study addresses the challenge of reliably estimating treatment effects and interactions in precision medicine clinical trials, where target subgroups often suffer from sparse sample sizes. To overcome this limitation, the authors propose a Bayesian framework that partially borrows information from external data sources—such as retrospective studies or early-phase trials—during both trial design and analysis. The approach assigns fitness-based weights to individual external observations through covariate distribution matching, enabling precise information borrowing. Innovatively integrating covariate matching, individual-level weighting, and Bayesian modeling, the method also incorporates design priors to determine sample size and decision boundaries. Simulation studies demonstrate its superior performance over existing dynamic borrowing strategies across diverse scenarios, yielding substantially improved accuracy in subgroup effect estimation. The framework is successfully illustrated through an application to a gastric cancer clinical trial design.

This study addresses the challenge of identifying and estimating causal effects under network interference, where an individual’s treatment may spill over and affect others’ outcomes. The authors propose a solution based on a linear outcome model that yields unbiased and consistent estimates of both binary and continuous treatment effects when the interference structure is known or partially known. The approach accommodates both fixed and random interference network specifications and innovatively eliminates interference-induced bias while remaining compatible with standard linear regression software. It also conveniently allows for the incorporation of random effects and heteroskedasticity- and autocorrelation-consistent (HAC) standard errors. Numerical simulations and empirical analyses demonstrate the method’s effectiveness in bias correction and practical applicability.

This study addresses the challenge of efficiently identifying practically meaningful treatment effects under resource constraints and concurrent experimentation, where conventional resource allocation strategies—optimized to minimize mean squared error (MSE)—often prove suboptimal. The authors propose a novel framework that shifts the objective toward minimizing the worst-case Type II error (i.e., miss rate) by leveraging statistical power. They develop a variance inflation mechanism with a correction factor, tailored to scenarios where outcome standard deviations are either known or estimated from pilot data, and formulate optimization models under three distinct risk criteria. A fully data-driven Surrogate-S algorithm is introduced to implement the approach without requiring ground-truth variance information. Theoretical analysis demonstrates the potential inefficiency of MSE-oriented strategies in detection tasks, while numerical experiments show that the proposed method achieves near-optimal performance using only pilot-based variance estimates.