A New Framework for Estimation of Unconditional Quantile Treatment Effects: The Residualized Quantile Regression (RQR) Model

Abstract

The opportunities for understanding how treatment effects vary across different segments of the population have led to a rise in the use of quantile regressions for identifying unconditional quantile treatment effects (QTEs). However, existing quantile regression models fall into two categories: those that are unsuitable for identifying unconditional QTEs and those that often struggle with the complex data structures common in sociology and other social sciences. In particular, existing methods face difficulties with large data sets and high-dimensional fixed effects. The authors introduce a two-step approach to estimating unconditional QTEs, which is easy to use and aligns with the needs of sociologists. First, the treatment variable is decomposed into a systematic and random part, and then, the random variation in the treatment status is used as the sole independent variable in a quantile regression model. Through a series of simulations and three empirical applications, the authors provide strong evidence that the residualized quantile regression (RQR) approach provides approximately unbiased estimates of unconditional QTEs comparable with existing methods. Moreover, the RQR approach offers greater flexibility and enhances computational speed compared with existing models, and it can easily handle high-dimensional fixed effects. In sum, the RQR approach fills a pressing void in quantitative research methodology, offering a much-needed tool for studying treatment effect heterogeneity.

The authors' abstract, as published at the source. Sociological Methodology, 2026 · DOI ↗