Testing Identifiability and Transportability with Observational and Experimental Data
This work addresses a critical problem in clinical research and decision-making by enabling more reliable causal inference across populations, though it is incremental as it builds on existing causal transportability and identifiability frameworks.
The authors tackled the challenge of transporting causal effects from experimental data in one population to another without requiring a known causal graph, by proposing a Bayesian method that assesses identifiability and transportability, and demonstrated improved estimation accuracy in simulations.
Transporting causal information learned from experiments in one population to another is a critical challenge in clinical research and decision-making. Causal transportability uses causal graphs to model differences between the source and target populations and identifies conditions under which causal effects learned from experiments can be reused in a different population. Similarly, causal identifiability identifies conditions under which causal effects can be estimated from observational data. However, these approaches rely on knowing the causal graph, which is often unavailable in real-world settings. In this work, we propose a Bayesian method for assessing whether Z-specific (conditional) causal effects are both identifiable and transportable, without knowing the causal graph. Our method combines experimental data from the source population with observational data from the target population to compute the probability that a causal effect is both identifiable from observational data and transportable. When this holds, we leverage both observational data from the target domain and experimental data from the source domain to obtain an unbiased, efficient estimator of the causal effect in the target population. Using simulations, we demonstrate that our method correctly identifies transportable causal effects and improves causal effect estimation.