Causal Inference for Quantile Treatment Effects

TL;DR

This paper introduces a new estimator for quantile treatment effects that accounts for various exposure types, providing asymptotic properties and practical methods for environmental impact analysis.

Contribution

It proposes the weighted QTE (WQTE) estimator with balancing weights and compares regression and weighted methods for causal inference on quantiles.

Findings

The WQTE estimator has desirable asymptotic properties.

Weighted methods outperform regression in finite samples.

Application to Bavarian Danube data estimates the 95% QTE of phosphorus on copper.

Abstract

Analyses of environmental phenomena often are concerned with understanding unlikely events such as floods, heatwaves, droughts or high concentrations of pollutants. Yet the majority of the causal inference literature has focused on modelling means, rather than (possibly high) quantiles. We define a general estimator of the population quantile treatment (or exposure) effects (QTE) -- the weighted QTE (WQTE) -- of which the population QTE is a special case, along with a general class of balancing weights incorporating the propensity score. Asymptotic properties of the proposed WQTE estimators are derived. We further propose and compare propensity score regression and two weighted methods based on these balancing weights to understand the causal effect of an exposure on quantiles, allowing for the exposure to be binary, discrete or continuous. Finite sample behavior of the three estimators is studied in simulation. The proposed methods are applied to data taken from the Bavarian Danube catchment area to estimate the 95% QTE of phosphorus on copper concentration in the river.