Diffusion Model in Causal Inference with Unmeasured Confounders
This addresses a key limitation in causal inference for researchers and practitioners dealing with observational data where not all confounders are measured, though it is incremental as it builds on existing diffusion-based approaches.
The paper tackles the problem of causal inference with unmeasured confounders by proposing an extended diffusion-based model, BDCM, which uses the backdoor criterion to handle unobserved variables, and synthetic experiments show it captures counterfactual distributions more precisely than prior methods.
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.