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Abstract:
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In observational study, the most salient challenge is to adjust for confounders to mimic randomized experiment. In the setting of more than two treatment levels, several generalized propensity score (GPS) models have been proposed to balance covariates among treatment groups. Those models assume some parametric forms for treatment variable distributions especially with constant variance assumption. With the existence of heteroskedasticity, the constant variance assumption might affect the existing propensity score methods and the causal effect of interest. In this paper, we propose a novel GPS method to handle non-constant variance in the treatment model by extending Xiao et al. (2020) with weighted least squares method. We conduct a set simulation studies and show that the proposed method outperforms in terms of covariate balance and low bias in causal effect estimates.
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