yangzhao98/synthate

Functions to enable synthetic causal estimation.

synthate package: Synthetic estimators for the average treatment effect

This package comprises a suite of tools to simulate various data-generating processes. Its purpose is to check the operating characteristics of various causal estimators and to optimally combine them.

Installation

The synthate package can currently be obtained from GitLab: https://code.rand.org/agniel-projects/personal-packages/synthate or from GitHub at https://github.com/denisagniel/synthate. It may be downloaded by running

devtools::install_github('denisagniel/synthate')

Overview

There are three main features of synthate:

• Simulating data
• Causal estimation
• Combining estimators

Simulating data

There are currently seven DGPs derived from six different papers to choose from:

Short-hand	Paper
ks	Kang and Schafer (Kang and Schafer (2007))
ld	Lunceford and Davidian (Lunceford et al. (2004))
iw	Waernbaum (Waernbaum (2012))
fi	Fan and Imai (Fan (2016))
pa	Austin (Austin (2009))
ls	Leacy and Stuart (Leacy and Stuart (2014))
ik	Iacus and King (Iacus, King, and Porro (2009))

References

Austin, Peter C. 2009. “Some methods of propensity-score matching had superior performance to others: results of an empirical investigation and monte carlo simulations.” Biometrical Journal 51 (1): 171–84. doi:10.1002/bimj.200810488.

Fan, Jianqing. 2016. “Improving Covariate Balancing Propensity Score : A Doubly Robust and Efficient Approach ∗,” 1–47.

Iacus, Stefano M., Gary King, and Giuseppe Porro. 2009. “<b>cem</b> : Software for Coarsened Exact Matching.” Journal of Statistical Software 30 (9). doi:10.18637/jss.v030.i09.

Kang, Joseph D. Y., and Joseph L. Schafer. 2007. “Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data.” Statistical Science 22 (4): 523–39. doi:10.1214/07-STS227.

Leacy, Finbarr P., and Elizabeth A. Stuart. 2014. “On the joint use of propensity and prognostic scores in estimation of the ATT: A simulation study.” Statistics in Medicine 33 (20): 161–69. doi:10.3851/IMP2701.Changes.

Lunceford, Jared K, Jared K Lunceford, Marie Davidian, and Marie Davidian. 2004. “Stratification and weighting via the propensity score in estimation of causal treatment e ects: a comparative study.” Statistics in Medicine 2960 (19): 2937–60. doi:10.1002/sim.1903.

Waernbaum, Ingeborg. 2012. “Model misspecification and robustness in causal inference: Comparing matching with doubly robust estimation.” Statistics in Medicine 31 (15): 1572–81. doi:10.1002/sim.4496.