experimentation

This study addresses the common reliance on unrealistic assumptions about average treatment effects in experimental and observational research designs. It proposes a novel paradigm that shifts focus from directly positing average effects to modeling the full distribution of individual treatment effects, from which more plausible assumptions about average effects can be derived. By integrating distributional modeling with cross-disciplinary case studies, the approach demonstrates its validity and utility across diverse fields—including medicine, economics, and psychology—offering researchers a principled, heterogeneity-aware framework for specifying effect sizes grounded in empirical realism rather than idealized assumptions.

This study addresses the persistent ambiguity in classifying repeated measures experimental designs, which often arises from conceptual confusion. To resolve this issue, the authors systematically clarify the core characteristics of such designs and propose a novel classification framework grounded in experimental units and randomization strategies. For the first time in this context, Hasse diagrams are introduced to visually represent the hierarchical structure of these designs. This approach effectively distinguishes among various types of repeated measures designs, eliminates terminological ambiguities, and substantially enhances both the rigor and interpretability of experimental planning and reporting.

In large-scale aggregate-unit experiments (e.g., markets), conventional randomized treatment assignment often yields severe baseline imbalance due to extremely few treated units, leading to biased causal estimates. To address this, we systematically integrate the synthetic control method into experimental design, proposing a non-randomized treatment allocation mechanism: dynamically constructing a weighted synthetic control group based on pre-treatment covariates. We further develop配套 components—including counterfactual prediction, distance-driven unit matching, robust variance estimation, and a novel confidence interval construction procedure. Theoretically, our estimator is proven consistent and asymptotically normal. Empirically, it reduces estimation bias by 40–65% relative to standard randomization and substantially improves statistical power. Our core contribution is a new causal inference paradigm for small-N aggregate experiments—rigorous in inference, unbiased under mild assumptions, and highly interpretable.

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.

Estimating causal effects from observational data requires selecting appropriate control and instrumental variables that satisfy causal identification conditions—a challenging task often reliant on strong domain knowledge or ad hoc assumptions. Method: This paper proposes the first end-to-end joint learning framework that automatically identifies valid combinations of control and instrumental variables. Grounded in conditional independence testing, the method integrates nonparametric dependence measures with structural search optimization, ensuring statistical consistency in variable selection under mild regularity conditions. Contribution/Results: Unlike conventional approaches requiring prespecified variable sets or strong prior assumptions, our framework is fully data-driven. In simulations, it achieves significantly higher variable identification accuracy. Empirically, applied to the Job Corps study, its estimated treatment effect closely aligns with results from the randomized controlled trial—demonstrating both validity and robustness in real-world causal inference.

This study challenges the conventional wisdom that representative sampling is always optimal in randomized controlled trials (RCTs) with limited budgets. It proposes a novel optimal sampling framework that integrates cost structures and prior knowledge of heterogeneous treatment effects. By combining Bayesian priors, heterogeneity modeling, hypothesis testing, and expected utility optimization, the authors theoretically demonstrate that under tight budget constraints, concentrating sampling efforts on a single high-potential subpopulation can substantially enhance the expected impact of downstream interventions. Only when the budget is sufficiently large does the optimal strategy converge to representative sampling. These findings hold across diverse resource-constrained experimental settings and offer a new paradigm for efficient, evidence-based decision-making in applied research.

This study addresses bias in causal inference arising from unmeasured confounding in observational studies by proposing a novel research design that integrates expert knowledge. Specifically, it actively identifies potential unmeasured confounders by querying clinical experts about differences in treatment intent between matched patient pairs. This approach systematically incorporates clinicians’ judgments on treatment intent into the confounding detection pipeline for the first time, establishing a theoretical foundation that transcends the limitations of methods relying solely on observed data. Combining propensity score matching, natural language processing of clinical notes, and a semi-synthetic validation framework, the method demonstrates significant unmeasured confounding in electronic health records within an ICU setting. Using clinical notes as proxies for physician knowledge, the authors validate the approach’s efficacy in an environment with known ground-truth causal effects.

This study addresses the bias in treatment effect estimation that arises in pragmatic trials using electronic health records when outcome assessments are uncontrolled, irregular, and potentially influenced by the intervention itself. Leveraging pre-trial cohort data, the authors developed a tailored simulation framework to systematically compare single-timepoint approaches with longitudinal models in handling intervention-dependent assessment timing. By incorporating linear mixed models with exponential correlation structures, time-varying intervention effects, and flexible post-baseline timepoint selection to estimate either specific or average treatment effects, the study demonstrates that naive methods ignoring assessment timing dependencies yield substantial bias. In contrast, longitudinal models accommodating flexible follow-up schedules produce unbiased estimates, with the linear mixed model featuring an exponential correlation structure exhibiting optimal performance—providing a critical analytical foundation for pragmatic trials such as MI-CARE.

This study addresses the challenge of identifying heterogeneous treatment effects while controlling the false discovery rate (FDR) in matched observational studies, where limited sample sizes and unmeasured confounding pose significant obstacles. The authors propose a novel method that adaptively discovers interpretable subgroups defined by covariate thresholds under many-to-one matching designs. Their approach achieves exact FDR control at the subgroup level for the first time and integrates sensitivity analysis models to account for unobserved confounding, leveraging multiple controls to enhance statistical power. Theoretical analysis, simulations, and empirical evaluation demonstrate that the method outperforms existing baselines in both accuracy and power when estimating heterogeneous economic returns to college education.