define causal estimands

In longitudinal studies, death often truncates non-fatal outcomes, rendering existing causal estimands either restricted to the survivor subpopulation or lacking a clear causal interpretation for the entire target population. This work proposes a novel class of marginal separable effects that, for the first time, defines a causally interpretable overall effect applicable to the full population, extending conditional separable effects into a population-level causal summary measure. Drawing on causal inference theory, we establish identification assumptions and develop an estimation approach that integrates longitudinal observational modeling with reweighting strategies. Reanalysis of a prostate cancer clinical trial demonstrates that different estimands can yield divergent conclusions about treatment efficacy, thereby highlighting the practical relevance and sensitivity of the proposed framework.

This paper addresses causal inference under network interference in a finite population. We propose an “agnostic” causal parameter—the Average Partial Causal Effect (APCE)—that requires no structural model assumptions and is identifiable and consistently estimable from a single network randomized experiment. Methodologically, we develop a structural-model-agnostic identification framework based on exposure mapping invariance, integrate inverse-probability weighting with design-based finite-population inference, and leverage local smoothness of equilibrium models to interpret APCE as a derivative-weighted average response—extending the Local Average Treatment Effect (LATE) paradigm. Our key contributions are: (i) the first definition of a globally interpretable, identifiable, and estimable intervention response measure under minimal interference assumptions; (ii) a departure from structural modeling, achieving both robustness and interpretability; and (iii) exact recovery of a derivative-weighted average of the response function under smooth equilibrium conditions, establishing a novel paradigm for network causal inference.

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.

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.

This paper addresses the challenge of nonparametrically identifying causal effects under conditional separation (CSE) in the presence of unmeasured confounding—a setting where conventional identification fails. We propose the first proximal identification framework accommodating unobserved confounders by leveraging proxy variables. Building on influence function theory and semiparametric efficiency bounds, we develop a novel estimator that is locally semiparametric efficient, consistent, and asymptotically linear. Our estimator flexibly incorporates modern machine learning methods—including neural networks and random forests—to estimate complex nuisance functions, achieving faster theoretical convergence rates than existing approaches. Extensive simulations and application to a cancer clinical trial demonstrate robust performance and high estimation accuracy. The method substantially improves reliability and practicality of causal effect estimation in post-treatment event settings, particularly when unmeasured confounding is plausible.

When longitudinal outcomes are truncated by death, causal effects become challenging to define and estimate, and existing methods often lack clear causal assumptions and appropriate estimands. This study develops a unified framework that clarifies the definitional challenges and identification assumptions underlying various causal estimands in the presence of truncation by death. It proposes an integrated characterization combining stratum-specific average causal effects with restricted mean survival time, thereby revealing the intrinsically multifactorial nature of the problem. Building on Bayesian inference, the authors derive corresponding estimation procedures and evaluate their performance through simulations and real data from a randomized controlled trial on amyotrophic lateral sclerosis. The results demonstrate that the proposed framework yields a more comprehensive and accurate assessment of treatment effects.

This study addresses the challenge of interpreting conditional causal effects in observational studies and randomized trials when post-treatment events—such as dropout, noncompliance, or death-induced truncation—complicate inference. Conventional approaches require measurement and adjustment for common causes of these events and the outcome. The authors propose a novel identification framework that circumvents this requirement by assuming treatment assignment and unobserved causes of the outcome generate post-treatment events through independent mechanisms. Under this assumption, conditional separable effects and survivor average causal effects can be identified without measuring or adjusting for shared confounders. Built upon structural causal models and the principle of independent causal mechanisms, the method overcomes the dependence on covariate measurement inherent in traditional strategies and demonstrates robust applicability across diverse settings, including truncation by death, differential noncompliance, and the birth weight paradox.

This study addresses the challenge of causal inference when the outcome variable is latent and can only be indirectly measured through multiple imperfect proxies. Conventional methods are vulnerable to measurement incomparability across studies and model misspecification. To overcome these limitations, the authors propose a design-oriented nonparametric framework that identifies and estimates the average treatment effect on the latent outcome under randomized experiments. The key innovation lies in constructing an identifiable nonparametric bridge function that flexibly accommodates differences in measurement systems across studies and nonlinear relationships among proxies, without imposing strong parametric assumptions on the measurement model. Coupled with a debiased estimation procedure, the proposed method substantially outperforms benchmarks such as principal component analysis and inverse covariance weighting in simulations, accurately recovering comparable and consistent causal effects on the latent variable while eliminating spurious cross-study heterogeneity.

This study addresses the problem of testing whether a treatment effect operates entirely through observed mediators and identifying causal mechanisms under control for covariates. The authors propose a statistical test based on double machine learning, extending— for the first time—the joint evaluation of full mediation and causal mechanism identification to non-randomized treatment settings. By integrating conditional independence testing, the method achieves root-n consistent and asymptotically normal inference even in the presence of high-dimensional covariates. Simulation studies demonstrate favorable finite-sample performance, and the approach is successfully applied to two randomized experiments examining maternal mental health and social norms.