Matching on Generalized Propensity Scores with Continuous Exposures

Understand

In the context of a binary treatment, matching is a well-established approach in causal inference.

• However, in the context of a continuous treatment or exposure, matching is still underdeveloped.
• We propose an innovative matching approach to estimate an average causal exposure-response function under the setting of continuous exposures that relies on the generalized propensity score (GPS).
• Our approach maintains the following attractive features of matching: a) clear separation between the design and the analysis; b) robustness to model misspecification or to the presence of extreme values of the estimated GPS; c) straightforward assessment of covariate balance.