Dear Workshop Community,
Our next meeting will be *Wednesday October 30*, where Xun Pang will
present research on* "**A Bayesian Generalized Synthetic Control Approach
for Causal Inference With TSCS Data: A Dynamic Latent Factor Model With
Hierarchical Shrinkage**"*.
*Abstract:* This paper proposes a Bayesian synthetic control method
(a.k.a., the latent multifactor model approach) for causal inference with
observational time-series cross-sectional (TSCS) data. We develop a
state-space latent factor model and make dynamic and multilevel extensions
to the widely-applied difference-in-differences estimator, the synthetic
control approach, and latent factor models. We adopt a fully Bayesian
prior-to-posterior approach to parameter estimation and counterfactual
prediction. Compared with existing frequentist approaches, our method has
several advantages. First, it assigns unit- and time-specific weights to
outcomes and features of the donor pool to flexibly model the response
surface and exploits high-order relationships between treated and control
time series. Secondly, by combining dense modeling with latent factor
analysis and sparse modeling with Bayesian shrinkage, the method achieves a
good balance between correcting bias and avoiding overfitting. Thirdly,
based on Bayesian posterior distributions of counterfactuals, the proposed
method generates easily interpretable finite-sample inference for causal
quantities at an individual, group, or global level, which has long been a
challenge for the synthetic control method and its extensions. As a
model-based semi-parametric approach, the proposed method is highly
flexible and relax restrictive requirements on the data structure. We test
the method with Monte Carlo simulations and apply it to several empirical
studies to illustrate how to implement the method and to compare it with
some widely-applied alternative approaches. Those applications demonstrate
that the proposed method can help the researcher test causal effect
generated by complicated causal mechanisms and with substantively important
and methodologically thorny timing issues.
*Where:* CGIS Knafel Building, Room K354 (see this link
<https://map.harvard.edu/?bld=04471&level=9> for directions).
*When: *Wednesday, October 30 at 12 noon - 1:30 pm.
All are welcome. Lunch will be provided.
Best,
Georgie
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