Transportability without Graphs: A Bayesian Approach to Identifying s-Admissible Backdoor Sets

TL;DR

This paper introduces a Bayesian approach that identifies s-admissible backdoor sets for causal transportability without needing the causal graph, combining observational and experimental data to improve causal effect estimation across populations.

Contribution

The authors propose a novel Bayesian method that finds s-admissible backdoor sets without the causal graph, using a greedy algorithm to enhance transportability in causal inference.

Findings

Successfully identifies transportability bias in simulations

Improves causal effect estimation accuracy

Performs better than existing methods in semi-synthetic data

Abstract

Transporting causal information across populations is a critical challenge in clinical decision-making. Causal modeling provides criteria for identifiability and transportability, but these require knowledge of the causal graph, which rarely holds in practice. We propose a Bayesian method that combines observational data from the target domain with experimental data from a different domain to identify s-admissible backdoor sets, which enable unbiased estimation of causal effects across populations, without requiring the causal graph. We prove that if such a set exists, we can always find one within the Markov boundary of the outcome, narrowing the search space, and we establish asymptotic convergence guarantees for our method. We develop a greedy algorithm that reframes transportability as a feature selection problem, selecting conditioning sets that maximize the marginal likelihood of experimental data given observational data. In simulated and semi-synthetic data, our method correctly identifies transportability bias, improves causal effect estimation, and performs favorably against alternatives.