Short-term and Long-term Causal Effect Estimation with Double-head Transformer

The study of long-term effects, especially when they differ significantly from short-term implications, is paramount in understanding the broader implications of any condition. Estimating these long-term causal effects is not just crucial but is also a pre-requisite for a comprehensive evaluation and effective decision-making in various domains, from healthcare to economics. Most contemporary models, equipped with advanced algorithms, have been able to successfully estimate these long-term effects using short-term surrogates, primarily based on the surrogate assumption. However, it's evident that challenges persist, especially concerning the long-term dependency and the causal structure of the data. Addressing these issues is imperative for enhancing the accuracy and reliability of the estimations. In light of these challenges, we introduce TransLTEE, a novel double-heads Transformer model. This model aims to rectify the vanishing gradient problem frequently encountered in the long-term treatment effect estimation with double-head RNNs. By strategically replacing RNNs with Transformers, TransLTEE offers a more robust solution. The inclusion of encoder and decoder blocks in TransLTEE is a deliberate design choice that facilitates learning the distribution distance between treatment groups and predicted outcomes at each timestep. This ensures a more precise estimation of both short-term and long-term causal effects, potentially revolutionizing the way we understand and interpret data.