mlsanityData to 7 Oct 2026

External Validity: From Do-Calculus to Transportability Across Populations

arXiv 1503.01603 ↗First posted 5 Mar 2015stat.ME cs.AIcited by 75

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Abstract (arXiv)

The generalizability of empirical findings to new environments, settings or populations, often called "external validity," is essential in most scientific explorations. This paper treats a particular problem of generalizability, called "transportability," defined as a license to transfer causal effects learned in experimental studies to a new population, in which only observational studies can be conducted. We introduce a formal representation called "selection diagrams" for expressing knowledge about differences and commonalities between populations of interest and, using this representation, we reduce questions of transportability to symbolic derivations in the do-calculus. This reduction yields graph-based procedures for deciding, prior to observing any data, whether causal effects in the target population can be inferred from experimental findings in the study population. When the answer is affirmative, the procedures identify what experimental and observational findings need be obtained from the two populations, and how they can be combined to ensure bias-free transport.

Key facts. Cited by 75 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Mar 2015.

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Citing papers per year: 2021: 16; 2022: 13; 2023: 15; 2024: 8; 2025: 8; 2026: 15.

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  1. 2021Towards Causal Representation Learningcited by 207
  2. 2021Causal Attention for Vision-Language Taskscited by 111
  3. 2023Nonparametric Identifiability of Causal Representations from Unknown Interventionscited by 105
  4. 2023CLadder: Assessing Causal Reasoning in Language Modelscited by 102
  5. 2021Independent mechanism analysis, a new concept?cited by 96
  6. 2021Visual Representation Learning Does Not Generalize Strongly Within the Same Domaincited by 60
  7. 2022Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Datacited by 42
  8. 2022From Statistical to Causal Learningcited by 40
  9. 2021Transporting Causal Mechanisms for Unsupervised Domain Adaptationcited by 38
  10. 2021A Review of Generalizability and Transportabilitycited by 36
  11. 2022Active Bayesian Causal Inferencecited by 34
  12. 2021Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fixcited by 27
38 more
  1. 2022Causal Inference Through the Structural Causal Marginal Problemcited by 22
  2. 2022Image-based Treatment Effect Heterogeneitycited by 20
  3. 2024Neural Causal Abstractionscited by 20
  4. 2021Causal Multi-Agent Reinforcement Learning: Review and Open Problemscited by 16
  5. 2021Deconfounded Visual Groundingcited by 16
  6. 2021A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sourcescited by 14
  7. 2023Multiply Robust Federated Estimation of Targeted Average Treatment Effectscited by 13
  8. 2023Generative Causal Representation Learning for Out-of-Distribution Motion Forecastingcited by 12
  9. 2022Falsification before Extrapolation in Causal Effect Estimationcited by 10
  10. 2023Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictionscited by 10
  11. 2023Offline Minimax Soft-Q-learning Under Realizability and Partial Coveragecited by 9
  12. 2024C2P: Featuring Large Language Models with Causal Reasoningcited by 8
  13. 2024Interpreting Low-level Vision Models with Causal Effect Mapscited by 8
  14. 2026Causality is Key for Interpretability Claims to Generalisecited by 8
  15. 2022Causal inference in drug discovery and developmentcited by 6
  16. 2021Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curvescited by 5
  17. 2024Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Populationcited by 5
  18. 2025Counterfactual Identifiability via Dynamic Optimal Transportcited by 5
  19. 2022Kernel methods for long term dose response curvescited by 4
  20. 2022Learning Invariant Representations under General Interventions on the Responsecited by 4
  21. 2023Out-of-Variable Generalization for Discriminative Modelscited by 4
  22. 2024Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual Banditscited by 4
  23. 2024A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Researchcited by 4
  24. 2021CausalX: Causal Explanations and Block Multilinear Factor Analysiscited by 3
  25. 2023Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimatorscited by 3
  26. 2023Knowledge Guided Representation Learning and Causal Structure Learning in Soil Sciencecited by 3
  27. 2024Learning Invariant Causal Mechanism from Vision-Language Modelscited by 3
  28. 2025Entropic Causal Inference: Graph Identifiabilitycited by 3
  29. 2022Targeted Optimal Treatment Regime Learning Using Summary Statisticscited by 2
  30. 2023Causal Deep Learningcited by 2
  31. 2025Can LLMs Help Improve Analogical Reasoning For Strategic Decisions? Experimental Evidence from Humans and GPT-4cited by 2
  32. 2025Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimationcited by 2
  33. 2025Causal Judge Evaluation: Calibrated Surrogate Metrics for LLM Systemscited by 2
  34. 2022Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnosticscited by 1
  35. 2023Causal Discovery with Stage Variables for Health Time Seriescited by 1
  36. 2025Meta-Dependence in Conditional Independence Testingcited by 1
  37. 2026Adversarial Pragmatics for AI Safety Evaluation: A Diagnostic Framework and Seed Benchmark for Language-Mediated Controlcited by 1
  38. 2021Switching Contexts: Transportability Measures for NLPcited by 0

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