Causal Inference for Preprocessed Outcomes with an Application to Functional Connectivity

• URL: http://arxiv.org/abs/2602.02240v1
• Date: Mon, 02 Feb 2026 15:48:34 GMT
• Title: Causal Inference for Preprocessed Outcomes with an Application to Functional Connectivity
• Authors: Zihang Wang, Razieh Nabi, Benjamin B. Risk,
• Abstract summary: We propose a semiparametric framework for causal inference with derived outcomes obtained after intra-subject processing.<n>We specialize the framework to a mediation setting and focus on the natural direct effect.<n>We apply our method to estimate the impact of stimulant medication on brain connectivity in children with autism spectrum disorder.
• Score: 1.7078619393681336
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In biomedical research, repeated measurements within each subject are often processed to remove artifacts and unwanted sources of variation. The resulting data are used to construct derived outcomes that act as proxies for scientific outcomes that are not directly observable. Although intra-subject processing is widely used, its impact on inter-subject statistical inference has not been systematically studied, and a principled framework for causal analysis in this setting is lacking. In this article, we propose a semiparametric framework for causal inference with derived outcomes obtained after intra-subject processing. This framework applies to settings with a modular structure, where intra-subject analyses are conducted independently across subjects and are followed by inter-subject analyses based on parameters from the intra-subject stage. We develop multiply robust estimators of causal parameters under rate conditions on both intra-subject and inter-subject models, which allows the use of flexible machine learning. We specialize the framework to a mediation setting and focus on the natural direct effect. For high dimensional inference, we employ a step-down procedure that controls the exceedance rate of the false discovery proportion. Simulation studies demonstrate the superior performance of the proposed approach. We apply our method to estimate the impact of stimulant medication on brain connectivity in children with autism spectrum disorder.

Related papers

• CLEAR: Causal Learning Framework For Robust Histopathology Tumor Detection Under Out-Of-Distribution Shifts [2.0327514588332996]
  Domain shift in histopathology poses a major challenge to the generalization ability of deep learning models.<n>We propose a novel causal-inference-based framework that leverages semantic features while mitigating the impact of confounders.<n>We validate our method on the CAMELYON17 dataset and a private histopathology dataset, demonstrating consistent performance gains across unseen domains.
  arXiv  Detail & Related papers  (2025-10-16T03:45:31Z)
• Beyond the ATE: Interpretable Modelling of Treatment Effects over Dose and Time [46.2482873419289]
  We propose a framework for modelling treatment effect trajectories as smooth surfaces over dose and time.<n>Our approach decouples the estimation of trajectory shape from the specification of clinically relevant properties.<n>We show that our method yields accurate, interpretable, and editable models of treatment dynamics.
  arXiv  Detail & Related papers  (2025-07-09T20:33:33Z)
• Data Fusion for Partial Identification of Causal Effects [62.56890808004615]
  We propose a novel partial identification framework that enables researchers to answer key questions.<n>Is the causal effect positive or negative? and How severe must assumption violations be to overturn this conclusion?<n>We apply our framework to the Project STAR study, which investigates the effect of classroom size on students' third-grade standardized test performance.
  arXiv  Detail & Related papers  (2025-05-30T07:13:01Z)
• Causal mediation analysis with one or multiple mediators: a comparative study [36.52655918884223]
  We consider parametric and non-parametric implementations of classical estimators for causal mediation analysis.<n>We assess several approaches in a comprehensive benchmark on simulated data.<n>As an example of application, we propose a thorough analysis of factors known to influence cognitive functions.<n>This analysis shows that for several physiological factors, such as hypertension and obesity, a substantial part of the effect is mediated by changes in the brain structure.
  arXiv  Detail & Related papers  (2025-05-12T08:10:50Z)
• Kernel-based estimators for functional causal effects [1.6749379740049928]
  We propose causal effect estimators based on empirical Fr'echet means and operator-valued kernels, tailored to functional data spaces.<n>These methods address the challenges of high-dimensionality, sequential ordering, and model complexity while preserving robustness to treatment misspecification.
  arXiv  Detail & Related papers  (2025-03-06T22:48:55Z)
• Higher-Order Causal Message Passing for Experimentation with Complex Interference [6.092214762701847]
  We introduce a new class of estimators based on causal message-passing, specifically designed for settings with pervasive, unknown interference.<n>Our estimator draws on information from the sample mean and variance of unit outcomes and treatments over time, enabling efficient use of observed data.
  arXiv  Detail & Related papers  (2024-11-01T18:00:51Z)
• Identification and multiply robust estimation in causal mediation analysis across principal strata [7.801213477601286]
  We consider assessing causal mediation in the presence of a post-treatment event. We derive the efficient influence function for each mediation estimand, which motivates a set of multiply robust estimators for inference.
  arXiv  Detail & Related papers  (2023-04-20T00:39:20Z)
• Interpretable Deep Causal Learning for Moderation Effects [0.0]
  We address the problem of interpretability and targeted regularization in causal machine learning models. We propose a novel deep counterfactual learning architecture for estimating individual treatment effects.
  arXiv  Detail & Related papers  (2022-06-21T11:21:09Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data [83.50281440043241]
  We study the problem of inferring heterogeneous treatment effects from time-to-event data. We propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations.
  arXiv  Detail & Related papers  (2021-10-26T20:13:17Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)
• Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects [61.03579766573421]
  We study estimation of individual-level causal effects, such as a single patient's response to alternative medication. We devise representation learning algorithms that minimize our bound, by regularizing the representation's induced treatment group distance. We extend these algorithms to simultaneously learn a weighted representation to further reduce treatment group distances.
  arXiv  Detail & Related papers  (2020-01-21T10:16:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.