@inproceedings{5b60f7396538485fa20973d9e3a50833,

title = "Propensity score estimation using variational method on spatial logistic regression",

abstract = "Propensity score can be described as a probability of certain treatments conditional to the given observed covariates. Propensity score is one of the known methods to allows an observational study emulating certain characteristics from that of a randomized trial. The most common method used to estimate this score is the logistic regression model. Logistic regression can be used to model the probability of a certain event. With the advancement that is happening to spatial statistics, one can also build a logistic regression model that takes into consideration to that of spatial dependence. Thus, accommodate the spatial effect that is likely happening on observation data that came from different places. Problem arises from this model, that is the estimation of the parameters on the spatial logistic model. EM algorithm which is needed for this problem, still requires another adjustment since the expectation in the E-step is not available in closed form. Variational method modification is then proposed as an alternative for this problem. This paper reviews the propensity score estimation using spatial logistic regression and discusses the variational method as an alternative method to tackle the problem in estimating the parameters on the spatial logistic regression model in a theoretical study.",

keywords = "Propensity score, spatial logistic regression, variational method",

author = "Rizka, {H. N.} and Y. Widyaningsih",

note = "Funding Information: This research was funded by Directorate of Research and Development of Universitas Indonesia (DRPM UI) as a grant of Publikasi Terindeks Internasional (PUTI) Saintekes 2020 No. NKB-2429/UN2.RST/HKP.05.00/2020. Publisher Copyright: {\textcopyright} 2021 Author(s).; 6th International Symposium on Current Progress in Mathematics and Sciences 2020, ISCPMS 2020 ; Conference date: 27-10-2020 Through 28-10-2020",

year = "2021",

month = jul,

day = "23",

doi = "10.1063/5.0059068",

language = "English",

series = "AIP Conference Proceedings",

publisher = "American Institute of Physics Inc.",

editor = "Ivandini, {Tribidasari A.} and Churchill, {David G.} and Youngil Lee and Alias, {Yatimah Binti} and Chris Margules",

booktitle = "Proceedings of the 6th International Symposium on Current Progress in Mathematics and Sciences 2020, ISCPMS 2020",

}