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hello welcome to the presentation of slim twin treatment effect estimation with longitudinal ehr data iamp;#39;m the presenter george zucchini in this presentation i will form formalize and introduce the problem of estimating treatment effect in the longitudinal setting iamp;#39;ll explain the challenges that we need to tackle in order to achieve this objective and illustrate why existing works are inadequate then i will introduce sync twin which is a new method that i and colleagues developed in our lab to make interpretable and trustworthy estimation in the longitudinal setting so letamp;#39;s get started in this work we consider estimating the causal individual treatment effect from observational data such as ehr in the partic in the problem setting we consider longitudinal outcome with point treatment as illustrated in the figure on the right so for all individuals in the study we assume that the treatment allocation was made at some moment in time illustrated as the bla
