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Replication Data for: A Bayesian Alternative to Synthetic Control for Comparative Case Studies

By Xun Pang, Licheng Liu and Yiqing Xu · as credited by the source

Published 2021-04-01 · Political Analysis Dataverse

Deposited on Harvard Dataverse doi:10.7910/DVN/B6SWA1 licence: see repository 1 file
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About this dataset

This paper proposes a Bayesian alternative to the synthetic control method for comparative case studies with a single or multiple treated units. We adopt a Bayesian posterior predictive approach to Rubin's causal model, which allows researchers to make inferences about both individual and average treatment effects on treated observations based on the empirical posterior distributions of their counterfactuals. The prediction model we develop is a dynamic multilevel model with a latent factor term to correct biases induced by unit-specific time trends. It also considers heterogeneous and dynamic …

Collection: Political Analysis Dataverse

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Cite

@dataset{pang2021,
  title     = {Replication Data for: A Bayesian Alternative to Synthetic Control for Comparative Case Studies},
  author    = {Pang, Xun and Liu, Licheng and Xu, Yiqing},
  year      = {2021},
  publisher = {Harvard Dataverse},
  doi       = {10.7910/DVN/B6SWA1},
  url       = {https://doi.org/10.7910/DVN/B6SWA1}
}

Research that uses it

No published research on E2ER uses this dataset yet.

Description

Data model
Discipline
Social sciences
Method family
Empirical (quantitative)Replication
Design
Synthetic control
Research stage
Data acquisition
Contributors
Xun Pang (Data curation); Licheng Liu (Data curation); Yiqing Xu (Data curation)
Usage
not used in published research yet
Source
Harvard Dataverse · Political Analysis Dataverse
Record
dataset:dataverse/10-7910-dvn-b6swa1 · JSON

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