causal estimand definition

This paper addresses the ambiguity and interpretive inconsistencies in the causal interpretation of the ICH 2017 estimand framework’s textual definitions. Grounded in the potential outcomes paradigm of causal inference, it provides the first systematic formal statistical definition of estimands, uniformly representing treatment strategies, clinical endpoints, and intercurrent events as computable causal estimand expressions. Methodologically, it integrates causal diagram modeling, structured estimand derivation, and semantic mapping to ICH guidelines, rigorously distinguishing the causal population (target of inference) from the analysis set (pragmatic data subset), and introduces a novel causal approach to handling intercurrent events. The resulting framework achieves mathematical precision, reproducibility, and operational standardization of estimand definition—thereby enhancing methodological transparency and strengthening causal rigor in clinical trial design. (149 words)

Estimating causal effects of multiple air pollutants on multiple health outcomes under unmeasured confounding remains a fundamental challenge in environmental epidemiology. Method: We propose the first joint partial identification framework tailored to the multi-treatment–multi-outcome setting. Leveraging the factor confounding assumption to model residual dependence, we introduce novel joint constraints across multiple estimands—tightening individual effect bounds—and establish conditions under which negative control variables enable point identification. Our method integrates factor modeling, partial identification set optimization, and robust numerical algorithms. Results: Empirical analysis on Medicare claims data demonstrates that estimated effects of pollutants—including PM₂.₅, NO₂, and O₃—on cardiovascular and respiratory outcomes exhibit robustness to unmeasured confounding. This work advances causal inference in environmental health by providing a principled, computationally tractable framework for bounding heterogeneous treatment effects in high-dimensional, confounded settings.

This study addresses the challenge of causal inference in N-of-1 behavioral health case studies, where unobserved confounding impedes valid estimation. The authors propose the Ω causal estimator, which achieves identification without measuring or adjusting for confounders by leveraging functional contrasts over the support set of the outcome variable, requiring only the positivity assumption. This approach pioneers a support-based—rather than distribution-based—framework for causal inference, integrating de Finetti’s subjective probability interpretation with a theory of intervention–observation support consistency. A recall-baseline substitution mechanism bridges support-level contrasts to mean-level causal effects. The method’s feasibility is demonstrated in a case study on cognitive behavioral therapy for anxiety, offering clinicians a practical and robust tool for individualized causal inference.

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.

In causal inference, inconsistencies in estimand definition across ideal randomized controlled trials (RCTs), target trials, and real-world observational studies undermine validity; current target trial frameworks often over-adapt to observational designs, deviating from ideal RCTs and introducing implicit bias. Method: We propose, for the first time, a triadic comparative framework anchored to the ideal trial, systematically integrating causal graph models with experimental design theory to enable bias溯源 (traceability) and normative analysis. Contribution/Results: Applied to respiratory epidemiology, our framework significantly improves completeness in bias identification and bridges the conceptual gap in estimand definition between observational studies and RCTs. It establishes an actionable methodological benchmark for observational causal inference, enhancing rigor, transparency, and comparability across study designs.

Traditional network meta-analysis often lacks a clearly defined causal target population, rendering effect estimates difficult to interpret causally. This work proposes a novel framework oriented toward causal estimands: it begins by explicitly specifying the target population and sources of heterogeneity, which naturally leads to an arm-based aggregation approach. For the first time, this method systematically integrates principles from causal inference into network meta-analysis, positioning arm-level aggregation as a necessary consequence of causal identification rather than a modeling choice, and eliminating reliance on the structure of the treatment network. A unified causal model based on aggregate data jointly incorporates effect modifiers and central effects to enable precise causal effect estimation. Numerical experiments demonstrate that the proposed estimator can yield substantially different conclusions from conventional methods in specific scenarios, thereby altering evidence interpretation.

This study addresses the limitations of individualized clinical decision-making, which is often constrained by the high internal validity but limited external applicability of randomized controlled trials (RCTs) and the strong representativeness yet susceptibility to confounding bias in real-world data (RWD). To overcome these challenges, the authors propose a multi-source data integration paradigm grounded in an explicit causal inference framework. This approach systematically combines RCT and RWD by rigorously defining estimands, ensuring comparability across data sources, and conducting sensitivity analyses. The resulting methodology enhances the reliability and evidentiary strength of treatment effect estimates while providing a practical, regulatory-compliant pathway for generating individualized treatment recommendations.

This study addresses the challenge of ensuring rigor in causal inference under multi-source heterogeneous data fusion by proposing a structured design paradigm grounded in the target trial framework. The approach explicitly incorporates the target population and its sampling model into the causal analysis, systematically integrating external controls, generalizability, and transportability assessments through data element alignment, transparent assumption articulation, and emulation of the target trial. Its key innovation lies in anchoring the entire framework to a precise definition of the target population, thereby identifying and mitigating irreconcilable conflicts across data sources. This strategy enhances both the reliability and interpretability of causal conclusions derived from complex, real-world data ecosystems.

This study addresses the challenge that the complexity of estimands and associated intercurrent event strategies in randomized clinical trials often hinders effective engagement of patients and other stakeholders in design decisions. To bridge this gap, the project introduces— for the first time—a systematic, multimedia toolkit that translates the estimand framework into accessible formats through visualization and plain-language communication. The toolkit includes instructional videos, infographics, and editable slide decks, which collectively lower the cognitive barrier for non-specialists. By enhancing comprehension, it empowers patients and stakeholders to meaningfully participate in selecting appropriate estimands during trial design. This approach establishes an innovative communication paradigm that advances patient-centered clinical research.

This study addresses a fundamental challenge in causal inference when the treatment variable is an aggregate of multiple fine-grained components. It demonstrates that standard instrumental variable (IV) estimators lack a clear causal interpretation in such settings, as they correspond to a well-defined aggregate causal effect only under strong and often implausible restrictions on the distribution of interventions across components. By integrating causal inference frameworks, IV theory, and explicit modeling of intervention distributions, the paper formally characterizes the conditions required for identification. The analysis reveals that conventional IV estimates typically do not map to any single causal parameter in realistic scenarios, thereby calling into question their widespread interpretability in social science and epidemiological research.