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MCPanel: Imputation Estimators for Causal Effects in Panel Data

Fork of Athey et al’s MCPanel package with additional imputation methods for causal inference in panel data. Supported imputation methods:

  • matching (via filling)
  • kernel matching (kernel PCA via kernlab followed by filling)
  • dynamic factor model (dfms)
  • sparse dynamic factor model (sparseDFM)
  • matrix completion
  • two-way FE / DID
  • synthetic control
  • horizontal ridge regression
  • vertical ridge regression

These methods can all be implemented using the imputation_ATT function. Inference can be performed using the bootstrap / jack-knife (when number of treated units > 1) using the boot library [implementation in progress].

Example

using Abadie, Diamond, Hainmueller (2010) California Prop 99 data.

# install.packages("apoorvalal/MCPanel")
pacman::p_load(MCPanel, synthdid) # for data
data(california_prop99)

imputation_ATT takes a panel data set, identifiers for unit, time, treatment, and outcome, and a vector of method names (must be supported by imputationY: includes "did", "dfm", "sdfm", "knn", "kknn", "nuclear", "hardimpute", "svdimpute", "optspace", "mc", "sc", "env", "enh").

# call all with defaults
est = imputation_ATT(
        california_prop99,
        "State", "Year", "treated", "PacksPerCapita",
        # method specific arguments are passed as lists
        # check docs for imputationY for details
        dfm_args = list(r = 4, p = 1),
        sdfm_args = list(r = 4, q = 2)
)
## Converged after 67 iterations.

# The print method returns ATT estimates.
print(est)
## ATT estimates

##               DFM        sparse DFM               knn        kernel KNN 
##            -55.30            -55.42            -26.69            -23.23 
##               did matrix completion synthetic control   elastic net (V) 
##            -27.35            -20.00            -19.46            -11.49 
##   elastic net (H) 
##            -18.86

The plot method makes an event study figure. The legend contains ATT estimates, and its width may need to be customized.

plot(est, prec = 2, twd = 5.5)

Factor models perform poorly here, as does difference in differences. Most other methods have good pre-trends and broadly agree.

References

Susan Athey, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, and Khashayar Khosravi. Matrix Completion Methods for Causal Panel Data Models [link]

Licheng Liu, Ye Wang, and Yiqing Xu. “A Practical Guide to Counterfactual Estimators for Causal Inference with Time‐Series Cross‐Sectional Data.” American Journal of Political Science (2022). link