Causal Effect Estimation after Propensity Score Trimming with Continuous Treatments

Understand

Propensity score trimming, which discards subjects with propensity scores below a threshold, is a common way to address positivity violations that complicate causal effect estimation.

• However, most works on trimming assume treatment is discrete and models for the outcome regression and propensity score are parametric.
• This work proposes nonparametric estimators for trimmed average causal effects in the case of continuous treatments based on efficient influence functions.
• For continuous treatments, an efficient influence function for a trimmed causal effect does not exist, due to a lack of pathwise differentiability induced by trimming and a continuous treatment.