Transformers Handle Endogeneity in In-Context Linear Regression
Haodong Liang Krishna Balasubramanian Lifeng Lai
We explore the capability of transformers to address endogeneity in in-context linear regression. Our main finding is that transformers inherently possess a mechanism to handle endogeneity effectively using instrumental variables (IV). First, we demonstrate that the transformer architecture can emulate a gradient-based bi-level optimization procedure that converges to the widely used two-stage least squares (2SLS) solution at an exponential rate. Next, we propose an in-context pretraining scheme and provide theoretical guarantees showing that the global minimizer of the pre-training loss achieves a small excess loss. Our extensive experiments validate these theoretical findings, showing that the trained transformer provides more robust and reliable in-context predictions and coefficient estimates than the 2SLS method, in the presence of endogeneity.
endogeneity, instrumental variables, transformers, bi-level gradient descent
Transformers handle endogeneity in in-context linear regression by implementing gradient-based instrumental variable regression.