Causal inference for quantile treatment effects

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 modeling 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 (PS). Asymptotic properties of the proposed WQTE estimators are derived. We further propose and compare PS 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.