Direct and stable weight adjustment in non‐experimental studies with multivalued treatments: analysis of the effect of an earthquake on post‐traumatic stress
In February of 2010, a massive earthquake struck Chile, causing devastation in certain parts of the country, a↵ecting other areas, and leaving territories untouched. Two months after the earthquake, Chile’s Ministry of Social Development re-interviewed a representative subsample to its National Socioeconomic Characterization Survey (CASEN), completed two months before the earthquake, thereby creating a prospective longitudinal survey with detailed information of the same individuals before and after the earthquake. In this paper, we use a new weighting method for non-experimental studies with multi-valued treatments to estimate the impact of levels of exposure to the earthquake on posttraumatic stress. Unlike common weighting approaches for multi-valued treatments, this new method does not require explicit modeling of the generalized propensity score and instead focuses on directly balancing the covariates across the multi-valued treatments with weights that have minimum variance. As a result, the weighting estimator is stable and approximately unbiased. Furthermore, the weights are constrained to avoid model extrapolation. We illustrate this new method in a simulation study, both with categorical and continuous treatments. The results show that directly targeting balance instead of explicitly modeling the treatment assignment probabilities, tends to provide the best results in terms of bias and root mean square error. Using this method, we estimate the impact of the intensity of the earthquake on posttraumatic stress. We implement this method in the new package msbw for R.