6 Result(s)
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Chapter and Conference Paper
Specifying Multilevel Mixture Selection Models in Propensity Score Analysis
Causal inference with observational data is challenging, as the assignment to treatment is often not random and people may have different reasons to receive or to be assigned to the treatment. Moreover, the an...
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Chapter and Conference Paper
Measuring the Heterogeneity of Treatment Effects with Multilevel Observational Data
Multilevel latent class analysis and mixture propensity score models have been implemented to account for heterogeneous selection mechanisms and for proper causal inference with observational multilevel data (...
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Chapter and Conference Paper
Causal Inference with Observational Multilevel Data: Investigating Selection and Outcome Heterogeneity
Causal inference with observational data is challenging, as the assignment to treatment is not random, and people may have different reasons to receive or be assigned to the treatment. The multilevel structure...
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Chapter and Conference Paper
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, ...
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Chapter and Conference Paper
Neural Networks for Propensity Score Estimation: Simulation Results and Recommendations
Neural networks have been noted as promising for propensity score estimation because they algorithmically handle nonlinear relationships and interactions. We examine the performance neural networks as compared...
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Chapter and Conference Paper
Doubly Robust Estimation of Treatment Effects from Observational Multilevel Data
When randomized experiments cannot be conducted, propensity score (PS) matching and regression techniques are frequently used for estimating causal treatment effects from observational data. These methods remo...
