This package implements the synthetic difference in difference estimator (SDID) for the average treatment effect in panel data, as proposed in Arkhangelsky et al (2019). We observe matrices of outcomes Y and binary treatment indicators W that we think of as satisfying Yij = Lij + τij Wij + εij. Here τij is the effect of treatment on the unit i at time j, and we estimate the average effect of treatment when and where it happened: the average of τij over the observations with Wij=1. All treated units must begin treatment simultaneously, so W is a block matrix: Wij = 1 for i > N0 and j > T0 and zero otherwise, with N0 denoting the number of control units and T0 the number of observation times before onset of treatment. This applies, in particular, to the case of a single treated unit or treated period.
This package is currently in beta and the functionality and interface is subject to change.
Some helpful links for getting started:
- The R package documentation contains usage examples and method reference.
- The online vignettes contains a gallery of plot examples.
- For community questions and answers around usage, see Github issues page.
Installation
The current development version can be installed from source using devtools.
devtools::install_github("synth-inference/synthdid")
Example
library(synthdid) # Estimate the effect of California Proposition 99 on cigarette consumption data('california_prop99') setup = panel.matrices(california_prop99) tau.hat = synthdid_estimate(setup$Y, setup$N0, setup$T0) se = sqrt(vcov(tau.hat, method='placebo')) sprintf('point estimate: %1.2f', tau.hat) sprintf('95%% CI (%1.2f, %1.2f)', tau.hat - 1.96 * se, tau.hat + 1.96 * se) plot(tau.hat)
References
Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg, Guido W. Imbens, and Stefan Wager. Synthetic Difference in Differences, 2019. [arxiv]