Building upon recent developments in spatial econometric models that address the misspecification of spatial weight matrices through adaptive LASSO techniques, my research aims to enhance the ...

This project implements a novel approach to causal inference using Graph Attention Networks (GAT) to synthesize instrumental variables for confounded treatment effect estimation. The system learns to ...

Assessing causal treatment effect on a time-to-event outcome is of key interest in many scientific investigations. Instrumental variable (IV) is a useful tool to mitigate the impact of endogenous ...

Sequential causal effect estimation has recently attracted increasing attention from research and industry. While the existing models have achieved many successes, there are still many limitations.

A brief description of the methods used by the SYSLIN procedure follows. For more information on these methods, see the references at the end of this chapter. There are two fundamental methods of ...

Several of the estimation methods supported by PROC MODEL are instrumental variables methods. There is no standard method for choosing instruments for nonlinear regression. Few econometric textbooks ...

Abstract: Treatment effect estimation from observational data is a fundamental problem in causal inference, and its critical challenge is to address the confounding bias arising from the confounders.