# A General Algorithm for Deciding Transportability of Experimental Results

arXiv: https://arxiv.org/abs/1312.7485 · HTML: https://mlsanity.com/paper/1312.7485/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2013.

## Built on by (most cited first, 17 of 17)

- [Causal Transportability for Visual Recognition](https://mlsanity.com/paper/2204.12363/) (2022)
- [Neural Causal Abstractions](https://mlsanity.com/paper/2401.02602/) (2024)
- [Causal reasoning in typical computer vision tasks](https://mlsanity.com/paper/2307.13992/) (2023)
- [Causality from Bottom to Top: A Survey](https://mlsanity.com/paper/2403.11219/) (2024)
- [Out-of-Variable Generalization for Discriminative Models](https://mlsanity.com/paper/2304.07896/) (2023)
- [Causal Judge Evaluation: Calibrated Surrogate Metrics for LLM Systems](https://mlsanity.com/paper/2512.11150/) (2025)
- [Causal Discovery with Stage Variables for Health Time Series](https://mlsanity.com/paper/2305.03662/) (2023)
- [Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation](https://mlsanity.com/paper/2509.21866/) (2025)
- [Causal Markov Boundaries](https://mlsanity.com/paper/2103.07560/) (2021)
- [A Causal Inference Framework for Leveraging External Controls in Hybrid Trials](https://mlsanity.com/paper/2305.08969/) (2023)
- [Switching Contexts: Transportability Measures for NLP](https://mlsanity.com/paper/2105.00823/) (2021)
- [Building Object-based Causal Programs for Human-like Generalization](https://mlsanity.com/paper/2111.12560/) (2021)
- [BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables](https://mlsanity.com/paper/2307.02891/) (2023)
- [Towards a Transportable Causal Network Model Based on Observational Healthcare Data](https://mlsanity.com/paper/2311.08427/) (2023)
- [Robust Domain Generalisation with Causal Invariant Bayesian Neural Networks](https://mlsanity.com/paper/2410.06349/) (2024)
- [Transferring Causal Effects using Proxies](https://mlsanity.com/paper/2510.25924/) (2025)
- [Advantages and limitations in the use of transfer learning for individual treatment effects in causal machine learning](https://mlsanity.com/paper/2512.16489/) (2025)

## Abstract (arXiv)

Generalizing empirical findings to new environments, settings, or populations is essential in most scientific explorations. This article treats a particular problem of generalizability, called "transportability", defined as a license to transfer information learned in experimental studies to a different population, on which only observational studies can be conducted. Given a set of assumptions concerning commonalities and differences between the two populations, Pearl and Bareinboim (2011) derived sufficient conditions that permit such transfer to take place. This article summarizes their findings and supplements them with an effective procedure for deciding when and how transportability is feasible. It establishes a necessary and sufficient condition for deciding when causal effects in the target population are estimable from both the statistical information available and the causal information transferred from the experiments. The article further provides a complete algorithm for computing the transport formula, that is, a way of combining observational and experimental information to synthesize bias-free estimate of the desired causal relation. Finally, the article examines the differences between transportability and other variants of generalizability.