Generalizing experimental results by leveraging knowledge of mechanisms

We show how experimental results can be generalized across diverse populations by leveraging knowledge of local mechanisms that produce the outcome of interest, only some of which may differ in the target domain. We use structural causal models and a refined version of selection diagrams to represent such knowledge, and to decide whether it entails the invariance ofprobabilities of causationacross populations, which then enables generalization. We further provide: (i) bounds for the target effect when some of these conditions are violated; (ii) new identification results for probabilities of causation and the transported causal effect when trials from multiple source domains are available; as well as (iii) a Bayesian approach for estimating the transported causal effect from finite samples. We illustrate these methods both with simulated data and with a real example that transports the effects of Vitamin A supplementation on childhood mortality across different regions.

Automated summary

This study demonstrates how to generalize experimental results across diverse populations using knowledge of local mechanisms, providing methods to differentiate causation probabilities in different scenarios, set boundaries for target effects, and identify new results for causation probabilities and transported causal effects from trials in multiple source domains. It also introduces a Bayesian approach for estimating transported causal effects from finite samples.