Variational Counterfactual Intervention Planning to Achieve Target Outcomes

Xin Wang, Shengfei Lyu, Chi Luo, Xiren Zhou, Huanhuan Chen

TL;DR: We introduce a novel counterfactual target achievement problem in temporal systems and propose VCIP to find optimal interventions by modeling achievement probability.

Abstract: A key challenge in personalized healthcare is identifying optimal intervention sequences to guide temporal systems toward target outcomes, a novel problem we formalize as counterfactual target achievement. In addressing this problem, directly adopting counterfactual estimation methods face compounding errors due to the unobservability of counterfactuals. To overcome this, we propose Variational Counterfactual Intervention Planning (VCIP), which reformulates the problem by modeling the conditional likelihood of achieving target outcomes, implemented through variational inference. By leveraging the g-formula to bridge the gap between interventional and observational log-likelihoods, VCIP enables reliable training from observational data. Experiments on both synthetic and real-world datasets show that VCIP significantly outperforms existing methods in target achievement accuracy.

Lay Summary: We introduce a novel counterfactual target achievement problem in temporal systems and propose VCIP to find optimal interventions by modeling achievement probability.

Primary Area: General Machine Learning->Causality

Keywords: causal inference, counterfactual reasoning, counterfactual target achievement

Submission Number: 1947