synthdid-package: synthdid: Synthetic Difference-in-Difference Estimation

In synth-inference/synthdid: Synthetic Difference-in-Difference Estimation

synthdid: Synthetic Difference-in-Difference Estimation

Description

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 Y[i,j] = L[i,j] + tau[i,j] W[i,j] + noise[i,j]. Here tau[i,j] 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 tau[i,j] over the observations with W[i,j]=1. All treated units must begin treatment simultaneously, so W is a block matrix: W[i,j] = 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.

See Also

Useful links:

• https://github.com/synth-inference/synthdid

• Report bugs at https://github.com/synth-inference/synthdid/issues

Examples

# 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)

synth-inference/synthdid documentation built on Jan. 26, 2024, 7:21 a.m.