Latent causal inference using the propensity score from latent class regression model
نویسندگان
چکیده
منابع مشابه
Multilevel Propensity Score Methods for Estimating Causal Effects: A Latent Class Modeling Strategy
Despite their appeal, randomized experiments cannot always be conducted, for example, due to ethical or practical reasons. In order to remove selection bias and draw causal inferences from observational data, propensity score matching techniques have gained increased popularity during the past three decades. Although propensity score methods have been studied extensively for single-level data, ...
متن کاملLatent class regression on latent factors.
In the research of public health, psychology, and social sciences, many research questions investigate the relationship between a categorical outcome variable and continuous predictor variables. The focus of this paper is to develop a model to build this relationship when both the categorical outcome and the predictor variables are latent (i.e. not observable directly). This model extends the l...
متن کاملLatent class regression model in IRLS approach
Keywords--Regress ion, Latent classes, Iteratively reweighted least squares. I. I N T R O D U C T I O N We consider simultaneous constructing of several regressions by subsets of a given data set. Such an approach corresponds to so-called latent class models known in various statistical applications [1-3]. Latent class techniques are applied in factor and scaling analyses [4-8], structural equa...
متن کاملAn application of Measurement error evaluation using latent class analysis
Latent class analysis (LCA) is a method of evaluating non sampling errors, especially measurement error in categorical data. Biemer (2011) introduced four latent class modeling approaches: probability model parameterization, log linear model, modified path model, and graphical model using path diagrams. These models are interchangeable. Latent class probability models express l...
متن کاملUsing latent outcome trajectory classes in causal inference.
In longitudinal studies, outcome trajectories can provide important information about substantively and clinically meaningful underlying subpopulations who may also respond differently to treatments or interventions. Growth mixture analysis is an efficient way of identifying heterogeneous trajectory classes. However, given its exploratory nature, it is unclear how involvement of latent classes ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Korean Journal of Applied Statistics
سال: 2017
ISSN: 1225-066X
DOI: 10.5351/kjas.2017.30.5.615