A Weighted Resampling Framework for Causal Transportability

Transferring well-studied causal knowledge to new environments and populations can accelerate scientific advancement by enabling reusable findings, i.e., the causal transportability. Recent work on causal transportability characterises when causal effects can be transferred across environments, but it emphasises the graphical criterion and symbolic transport formulas whose practical use requires nontrivial numerical computation and estimation, especially under high-dimensional variables and nested expressions. To bridge this gap, we propose transportable causal sampling, a general weighted resampling-based framework that compiles transport formulas into actionable target-domain deconfounding procedures. We derive causal transport weights from known causal relationships and partial observations of key variables in the new environment, enabling a family of weighted samplers to generate synthetic samples that approximate target-domain interventional distributions. This provides a practical pathway from transport formulas to sample-level intervention simulation and downstream applications such as causal machine learning model training. Empirically, we demonstrate effectiveness on synthetic and semi-synthetic benchmarks.