Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements

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Entropy Balancing for Continuous Treatments (2020, methodology): “5 Moreover, Fong et al. (2018) also provide a non-parametric version – denoted as npCBGPS for the remainder of the paper. This approach obviates the need to specify a parametric structure for balancing weights by maximizing the empirical likelihood (Owen, 1990; Qin and Lawless, 1994) subject to imba”
A Survey on Causal Inference (2020, methodology): “ parametric propensity score, which increase the robustness to the misspecification of the propensity score model. An extension of CBPS is the covariate balancing generalized propensity score (CBGPS) [39], which enables to handle the treatment with continuous value. Due to the continuous valued trea”
Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments (2019, unknown): “For a future extension, our DML estimator serves as the preliminary element for policy learning and optimization with a continuous decision, following Manski (2004), Hirano and Porter (2009), Kitagawa and Tetenov (2018), Kallus and Zhou (2018), Demirer et al.”