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A General Algorithm for Deciding Transportability of Experimental Results
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Marginal Structural Models and Causal Inference in Epidemiology
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A Structural Approach to Selection Bias
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Compound Treatments and Transportability of Causal Inference
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Compound Treatments, Transportability, and the Structural Causal Model
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Experiments, quasi-experiments, and case studies: A review of empirical methods for evaluating decision support systems
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A Synthetic Data Integration Framework to Leverage External Summary-Level Information from Heterogeneous Populations
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Artificial intelligence methods for a Bayesian epistemology‐powered evidence evaluation
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Generalizing Trial Evidence to Target Populations in Non-Nested Designs: Applications to AIDS Clinical Trials
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Generalizing causal knowledge
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BaBE: Enhancing Fairness via Estimation of Explaining Variables
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External Validity
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Introduction to the Symposium: Causal Inference and Public Health
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Generalizability and Transportability of the National Lung Screening Trial Data: Extending Trial Results to Different Populations
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Estimating the Effects of Cancer Screening in Clinical Practice Settings: The Role of Selective Uptake and Suboptimal Adherence along the Cancer Screening Continuum
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Generalizing Treatment Effect Estimates From Sample to Population: A Case Study in the Difficulties of Finding Sufficient Data
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Improving transportability of randomized controlled trial inference using robust prediction methods
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Generalizability of Randomized Trial Results to Target Populations
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University of Pennsylvania 7th annual conference on statistical issues in clinical trials: Current issues regarding the use of biomarkers and surrogate endpoints in clinical trials (morning panel discussion)
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From Experiments to Policy Insights: Generalizing Causal Effects From Study Samples to Target Populations
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How transportability analysis can be useful for cumulative theory testing in management research
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When the parts are greater than the whole: how understanding mechanisms can advance implementation research
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6. Generalizability of Clinical Trials Results
inferred← citeshttps://doi.org/10.1214/14-sts486openalex-snapshot
External Validity: From Do-Calculus to Transportability Across Populations
inferred← citeshttps://doi.org/10.1214/23-sts889openalex-snapshot
Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review
inferred← citeshttps://doi.org/10.1287/mnsc.2023.03544openalex-snapshot
The Task Space: An Integrative Framework for Team Research
inferred← citeshttps://doi.org/10.1289/ehp13961openalex-snapshot
Bridging Differences in Cohort Analyses of the Relationship between Secondhand Smoke Exposure during Pregnancy and Birth Weight: The Transportability Framework in the ECHO Program
inferred← citeshttps://doi.org/10.1515/em-2016-0014openalex-snapshot
The Choice of Effect Measure for Binary Outcomes: Introducing Counterfactual Outcome State Transition Parameters
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Generalizing Experimental Findings
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Semi-Parametric Estimation and Inference for the Mean Outcome of the Single Time-Point Intervention in a Causally Connected Population
inferred← citeshttps://doi.org/10.1515/jci-2021-0059openalex-snapshot
Causal effect on a target population: A sensitivity analysis to handle missing covariates
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Current philosophical perspectives on drug approval in the real world
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Where’s the Rigor When You Need It?
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Recovering Causal Effects from Selection Bias
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Learning Adjustment Sets from Observational and Limited Experimental Data
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Causal Inference in HIV/AIDS Research: Generalizability and Applications
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Transporting experimental results with entropy balancing
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INTELLIGENT PSYCHO-DIAGNOSTIC FORECASTING SYSTEM
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Identifying Causal Effects with the R Package causaleffect
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Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach
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A Study on the Influence of Project Leadership on Project Management and Performance
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Generalizing Experimental Findings
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Causal knowledge and the process of policy making: towards a bottom-up approach
inferred← citeshttps://doi.org/10.26509/frbc-wp-201310ropenalex-snapshot
Covariates and Causal Effects: The Problem of Context
inferred← citeshttps://doi.org/10.26616/nioshpub2020106openalex-snapshot
Current intelligence bulletin 69: NIOSH practices in occupational risk assessment.
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Current intelligence bulletin 69: NIOSH practices in occupational risk assessment.
inferred← citeshttps://doi.org/10.3102/1076998614558486openalex-snapshot
How Generalizable Is Your Experiment? An Index for Comparing Experimental Samples and Populations
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Tasks Beyond Taxonomies: A Multidimensional Design Space for Team Tasks
inferred← citeshttps://doi.org/10.31234/osf.io/5wsnaopenalex-snapshot
Generalizability Crisis Meets Heterogeneity Revolution: Determining Under Which Boundary Conditions Findings Replicate and Generalize
inferred← citeshttps://doi.org/10.31234/osf.io/b23k7openalex-snapshot
Methods in Causal Inference Part 1: Causal Diagrams and Confounding
inferred← citeshttps://doi.org/10.31234/osf.io/vr268openalex-snapshot
Methods in Causal Inference Part 2: Interaction, Mediation, and Time-Varying Treatments
inferred← citeshttps://doi.org/10.3386/w22595openalex-snapshot
Understanding and Misunderstanding Randomized Controlled Trials
inferred← citeshttps://doi.org/10.3386/w25451openalex-snapshot
The Dozen Things Experimental Economists Should Do (More of)
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Transporting results in an observational epidemiology setting: purposes, methods, and applied example
inferred← citeshttps://doi.org/10.3390/cancers14163923openalex-snapshot
A Causal Framework for Making Individualized Treatment Decisions in Oncology
inferred← citeshttps://doi.org/10.3390/ijerph20032404openalex-snapshot
Fast Methods for Drug Approval: Research Perspectives for Pandemic Preparedness
inferred← citeshttps://doi.org/10.3390/math10224326openalex-snapshot
A Bayesian Causal Model to Support Decisions on Treating of a Vineyard
inferred← citeshttps://doi.org/10.3790/schm.2025.401889openalex-snapshot
Ex Ante Facts, Causal Structures, and Prediction
inferred← citeshttps://doi.org/10.48550/arxiv.1701.06739openalex-snapshot
Partial Bridging of Vaccine Efficacy to New Populations
inferred← citeshttps://doi.org/10.48550/arxiv.1806.07082openalex-snapshot
Simplifying Probabilistic Expressions in Causal Inference
inferred← citeshttps://doi.org/10.48550/arxiv.1903.03448openalex-snapshot
Support and Invertibility in Domain-Invariant Representations
inferred← citeshttps://doi.org/10.48550/arxiv.2105.00823openalex-snapshot
Switching Contexts: Transportability Measures for NLP
inferred← citeshttps://doi.org/10.48550/arxiv.2106.03024openalex-snapshot
Causal aggregation: estimation and inference of causal effects by constraint-based data fusion
inferred← citeshttps://doi.org/10.48550/arxiv.2111.12560openalex-snapshot
Building Object-based Causal Programs for Human-like Generalization
inferred← citeshttps://doi.org/10.51744/cip2openalex-snapshot
Designing evaluations to provide evidence to inform action in new settings
inferred← citeshttps://doi.org/10.57264/cer-2024-0101openalex-snapshot
Advancing the role of real-world evidence in comparative effectiveness research
inferred← citeshttps://doi.org/10.64898/2026.02.19.26346539openalex-snapshot
Agentic Trial Emulation to Learn Health System-specific Drug Effects At Scale
inferred← citeshttps://doi.org/10.64898/2026.03.03.709251openalex-snapshot
Transporting Causal Effects in Ecology: Concepts and Tools
inferred← citeshttps://doi.org/10.70675/11bb1cf9za376z4336z8998z647534b40e3eopenalex-snapshot
Generalizing a causal effect from a trial to a target population : methodological and theoretical contributions
inferred← citeshttps://doi.org/10.70675/2f432d6cz11f8z4d0bza530zbc10fbe668bcopenalex-snapshot
Causal Inference from Heterogeneous Data with Missing Data : Application to Critical Care Management
inferred← citeshttps://doi.org/10.7302/2658openalex-snapshot
Statistical Methods to Incorporate External Summary-level Information into a Current Study
inferred← citeshttps://doi.org/10.7916/d8-2x8p-6e09openalex-snapshot
Design-based, Bayesian Causal Inference for the Social-Sciences
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