Conditional Tensor Diffusion: Distributional Counterfactual Learning and Inference
Causal inference guides operational and managerial decisions but remains challenging in high-dimensional panel or tensor settings, where decisions may depend on the joint conditional distribution of missing control outcomes. We develop \emph{Counterfactual Tucker Diffusion} (\CFTDiff), which integrates the treatment mask and latent Tucker structure into conditional diffusion to recover this distribution given observed control outcomes through efficient nonlinear score learning in a low-dimensional core. The masked Tucker score preserves dependence across tensor modes while reducing the dimension of nonlinear score learning from the product of mode dimensions to the much smaller product of...
| Authors | Xinbing Kong, Zeyu Li, Junfan Mao et al. |
|---|---|
| Primary category | stat.ML |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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