Abstract: Estimating time-varying causal effects from real-world data attracts growing attention due to the high cost of performing interventional experiments and the wide availability of observational data. However, counterfactual data are not accessible, and true calculation of causal effects cannot be performed at the individual level. This paper proposes a linear algebraic framework to generate synthetic counterfactual data that exactly matches pretreatment factual data. Receiving treatment at a time $T_0$ cannot cause any difference reversely to counterfactual generation at $t < T_0$. If we hold this strictness on counterfactual generation, our approach claims the first-ever counterfactual generative model to create personalized clinical trial digital twins. Moreover, using simulated ground truth counterfactual data, we show that our method greatly outperforms the most cited methods of counterfactual generation and individual treatment effect estimation. We also provide a formula that can estimate the time-varying variance of individual treatment effects, interpreted as a confidence of generated counterfactuals to true values.