  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.
