augsynth: Fit Augmented SCM

In ebenmichael/augsynth: The Augmented Synthetic Control Method

Fit Augmented SCM

Description

Fit Augmented SCM

Usage

augsynth(form, unit, time, data, t_int = NULL, ...)

Arguments

form	outcome ~ treatment | auxillary covariates
unit	Name of unit column
time	Name of time column
data	Panel data as dataframe
t_int	Time of intervention (used for single-period treatment only)
...	Optional arguments Single period augsynth with/without multiple outcomes "progfunc"What function to use to impute control outcomes: Ridge=Ridge regression (allows for standard errors), None=No outcome model, EN=Elastic Net, RF=Random Forest, GSYN=gSynth, MCP=MCPanel, CITS=CITS, CausalImpact=Bayesian structural time series with CausalImpact, seq2seq=Sequence to sequence learning with feedforward nets "scm"Whether the SCM weighting function is used "fixedeff"Whether to include a unit fixed effect, default F "cov_agg"Covariate aggregation functions, if NULL then use mean with NAs omitted Multi period (staggered) augsynth "relative"Whether to compute balance by relative time "n_leads"How long past treatment effects should be estimated for "n_lags"Number of pre-treatment periods to balance, default is to balance all periods "alpha"Fraction of balance for individual balance "lambda"Regularization hyperparameter, default = 0 "force"Include "none", "unit", "time", "two-way" fixed effects. Default: "two-way" "n_factors"Number of factors for interactive fixed effects, default does CV

Value

augsynth object that contains:

• "weights"weights

• "data"Panel data as matrices

ebenmichael/augsynth documentation built on March 20, 2024, 5:20 a.m.