TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective
This work addresses causal inference for decision-making in fields like healthcare or policy, though it appears incremental as it builds on existing seq2seq and contrastive learning techniques.
The paper tackled the problem of estimating causal effects of multiple treatments over time from individual data, addressing biases and interactions, and demonstrated improved accuracy in predicting outcomes and selecting optimal treatments compared to state-of-the-art methods on real-world datasets.
Determining causal effects of temporal multi-intervention assists decision-making. Restricted by time-varying bias, selection bias, and interactions of multiple interventions, the disentanglement and estimation of multiple treatment effects from individual temporal data is still rare. To tackle these challenges, we propose a comprehensive framework of temporal counterfactual forecasting from an individual multiple treatment perspective (TCFimt). TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions which further improves estimation accuracy. Through implementing experiments on two real-world datasets from distinct fields, the proposed method shows satisfactory performance in predicting future outcomes with specific treatments and in choosing optimal treatment type and timing than state-of-the-art methods.