Vector-Based Kernel Weighting: A Simple Estimator for Improving Precision and Bias of Average Treatment Effects in Multiple Treatment Settings

Treatment effect estimation must account for endogeneity, in which factors affect treatment assignment and outcomes simultaneously. By ignoring endogeneity, we risk concluding that a helpful treatment is not beneficial or that a treatment is safe when actually harmful. Propensity score (PS) matching or weighting adjusts for observed endogeneity, but matching becomes impracticable with multiple treatments, and weighting methods are sensitive to PS model misspecification in applied analyses. We used Monte Carlo simulations (1,000 replications) to examine sensitivity of multi-valued treatment inferences to PS weighting or matching strategies. We consider four variants of PS adjustment: inverse probability of treatment weights (IPTW), kernel weights, vector matching, and a new hybrid –vector-based kernel weighting (VBKW). VBKW matches observations with similar PS vectors, assigning greater kernel weights to observations with similar probabilities within a given bandwidth. We varied degree of PS model misspecification, sample size, number of treatment groups, and sample distribution across treatment groups. Across simulations, VBKW performed equally or better than the other methods in terms of bias and efficiency. VBKW may be less sensitive to PS model misspecification than other methods used to account for endogeneity in multi-valued treatment analyses.