babylonpolice.com

We study how to extend the use of the diffusion model to answer the causal
question from the observational data under the existence of unmeasured
confounders. In Pearl's framework of using a Directed Acyclic Graph (DAG) to
capture the causal intervention, a Diffusion-based Causal Model (DCM) was
proposed incorporating the diffusion model to answer the causal questions more
accurately, assuming that all of the confounders are observed. However,
unmeasured confounders in practice exist, which hinders DCM from being
applicable. To alleviate this limitation of DCM, we propose an extended model
called Backdoor Criterion based DCM (BDCM), whose idea is rooted in the
Backdoor criterion to find the variables in DAG to be included in the decoding
process of the diffusion model so that we can extend DCM to the case with
unmeasured confounders. Synthetic data experiment demonstrates that our
proposed model captures the counterfactual distribution more precisely than DCM
under the unmeasured confounders.