Causal Effect Identification and Inference with Endogenous Exposures and a Light-tailed Error

TL;DR

This paper introduces a new method for causal effect estimation with endogenous exposures by leveraging extreme quantiles and light-tailed errors, avoiding auxiliary variables and parametric assumptions.

Contribution

It presents a novel identification strategy and an estimation method based on extreme quantile regression that handles endogeneity without auxiliary variables.

Findings

Effective in estimating causal effects with endogenous exposures

Consistent and asymptotically normal estimator under general conditions

Validated through simulations and real data analysis

Abstract

Endogeneity poses significant challenges in causal inference across various research domains. This paper proposes a novel approach to identify and estimate causal effects in the presence of endogeneity. We consider a structural equation with endogenous exposures and an additive error term. Assuming the light-tailedness of the error term, we show that the causal effect can be identified by contrasting extreme conditional quantiles of the outcome given the exposures. Unlike many existing results, our identification approach does not rely on additional parametric assumptions or auxiliary variables. Building on the identification result, we develop a new method that estimates the causal effect using extreme quantile regression. We establish the consistency of the proposed extreme-based estimator under a general additive structural equation and demonstrate its asymptotic normality in the linear model setting. Simulations and data analysis of an automobile sale dataset show the effectiveness of our method in handling endogeneity.