# Generalizing experimental findings: identification beyond adjustments

arXiv: https://arxiv.org/abs/2206.06699 · HTML: https://mlsanity.com/paper/2206.06699/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 10 of 10)

- [Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder](https://mlsanity.com/paper/1609.08816/) (2016)
- [External Validity: From Do-Calculus to Transportability Across Populations](https://mlsanity.com/paper/1503.01603/) (2015)
- [Causal inference methods for combining randomized trials and observational studies: a review](https://mlsanity.com/paper/2011.08047/) (2020)
- [A Review of Generalizability and Transportability](https://mlsanity.com/paper/2102.11904/) (2021)
- [Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals](https://mlsanity.com/paper/1709.04589/) (2017)
- [Identifying Causal Effects via Context-specific Independence Relations](https://mlsanity.com/paper/2009.09768/) (2020)
- [Identifying Causal Effects with the R Package causaleffect](https://mlsanity.com/paper/1806.07161/) (2018)
- [Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach](https://mlsanity.com/paper/1902.01073/) (2019)
- [Estimation of causal effects with small data in the presence of trapdoor variables](https://mlsanity.com/paper/2003.03187/) (2020)
- [Do-search -- a tool for causal inference and study design with multiple data sources](https://mlsanity.com/paper/2007.08189/) (2020)

## Abstract (arXiv)

We aim to generalize the results of a randomized controlled trial (RCT) to a target population with the help of some observational data. This is a problem of causal effect identification with multiple data sources. Challenges arise when the RCT is conducted in a context that differs from the target population. Earlier research has focused on cases where the estimates from the RCT can be adjusted by observational data in order to remove the selection bias and other domain specific differences. We consider examples where the experimental findings cannot be generalized by an adjustment and show that the generalization may still be possible by other identification strategies that can be derived by applying do-calculus. The obtained identifying functionals for these examples contain trapdoor variables of a new type. The value of a trapdoor variable needs to be fixed in the estimation and the choice of the value may have a major effect on the bias and accuracy of estimates, which is also seen in simulations. The presented results expand the scope of settings where the generalization of experimental findings is doable