causal-inference

Documents about causal-inference

• DEVELOPING JUST-IN-TIME ADAPTIVE INTERVENTIONS IN MOBILE HEALTH USING LONGITUDINAL ZERO-INFLATED COUNT AND FACTORIAL DATA#348235ScholarBank@NUS
• Why the Big Five Personality Traits Are Composites, not Common Causes: Implications for Measurement, Prediction, and Causal Inference#121559OSF
• Optimocracy: Causal Inference on Cross-Jurisdictional Policy Data to Maximize Median Health and Wealth#161115Zenodo (CERN)
• A Continuous-Time Ensemble Kalman-Bucy Smoother for Causal Inference and Model Discovery#1011086arXiv (All)
• MR2G: A novel framework for causal network inference using GWAS summary data#228206PLOS
• Context-Dependent Resource Conversion: Estimands and Decompositions in the Capability-Ecological Dynamic Model#631855OSF
• On the Specificity, Causality, and Reliability of Electrophysiological Correlates of Human Memory: Limitations and Opportunities#637513ScholarlyCommons at Penn
• When Can Associations Be Interpreted as Evidence for Psychological Theory? Closing the Theory-to-Model Gap in Observational Research#393507OSF
• METHODOLOGICAL INNOVATIONS FOR CLINICAL RESEARCH: IMPROVING RELIABILITY, DATA SHARING, AND AGENTIC AI ACROSS THE EVIDENCE LIFECYCLE#929157ScholarlyCommons
• Identifying research particiaption effects through qualitative methods: Feedback from Research Engagement Consultants involved in a pediatric mental health comparative effectiveness trial#20023DOAJ
• Context-Dependent Resource Conversion: Estimands and Decompositions in the Capability-Ecological Dynamic Model#631944SocArXiv
• Evaluating the impact of an Active Labour Market Policy on employment: Short- and long-term perspectives#947831Repositorio de Universidad Autónoma de Chile.
• The Optimal Budget Generator: A Causal Inference Protocol for Maximizing Median Health and Wealth Through Public Goods Funding#171149Zenodo (CERN)
• The results of Transcriptome-wide Mendelian Randomization (TWMR) in large-scale populations can directly validate, across scales, the results of causal inference from deep learning combined with double machine learning on single-cell transcriptomes of human samples.#18872bioRxiv / medRxiv
• On Model Interpretability and Time-Reversal Testing in Analyses of Reciprocal Effects: Clarifying Modeling Issues in Sorjonen et al.’s (2025a, 2025b) Commentaries#231628HAL (France)
• Supplementary material that shows extra information for DAG building at each stage and the full DAGs from Directed acyclic graphs (DAGs) for causal inference of the impact of maternal and child helminth infections on child growth#211426LSHTM Data Compass
• Evaluating Pre-trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference#451752Duke Law Scholarship Repository
• Selection bias and inferential validity in fever of unknown origin studies. Comment on: Poposki et al. Visceral leishmaniasis as a leading cause of fever of unknown origin in immunocompetent adults#328943ARPHA OAI-PMH Endpoint
• The Structural Failure of Passive Observation and Recovery by Source-Independent Action: Minimal Results in a Finite, Elementary Setting#302269OSF
• Causality guided machine learning model on wetland CH4 emissions across global wetlands#683913eScholarship
• Job strain and ischemic heart disease: the balance of methodological bias and implications for prevention. Response to: Bonde JP et al. The demands-control-support work stress model and risk of ischemic heart disease: causal inference based on observational epidemiology.#347733HAL (France)