bpCausal-package: Bayesian Causal Inference with TSCS Data

bpCausal-packageR Documentation

Bayesian Causal Inference with TSCS Data

Description

The package provides functions for estimating and making inference of treatment effects from time-series cross-sectional data with the Bayesian dynamic multilevel latent factor models.

Details

The package provides functions for estimating and making inference of treatment effects from time-series cross-sectional data with the Bayesian dynamic multilevel latent factor models. Hierarchical shrinkage priors can be assigned for variable selection and determining the number of factors. It can accommodate both balanced and unbalanced panel data.

Author(s)

Licheng Liu; Xun Pang; Yiqing Xu

Maintainer: Licheng Liu

References

A Bayesian Alternative to Synthetic Control for Comparative Case Studies. Pang et. al (2021).

Examples

library(bpCausal)
data(bpCausal)
out <- bpCausal(data = simdata, index = c("id", "time"), 
               Yname = "Y", Dname = "D", 
               Xname = c("X1", "X2"), Zname = NULL, Aname = c("X1", "X2"), 
               re = "time", r = 10, niter = 20000)

liulch/bpCausal documentation built on Jan. 16, 2024, 10:59 p.m.