WARNING: This function is still in development and has not been tested throughly.
Following Aral et al. (2009), netmatch
computes matching
estimators for network data. The function netmatch_prepare
, which
prepares the data to be used with matchit
from
the MatchIt package, is called by netmatch
.
netmatch_prepare( dat, graph, timevar, depvar, covariates, treat_thr = rep(1L, length(graph)), adopt_thr = rep(1L, length(graph)), expo_pcent = FALSE, expo_lag = 0L ) netmatch( dat, graph, timevar, depvar, covariates, treat_thr = rep(1L, length(graph)), adopt_thr = rep(1L, length(graph)), expo_pcent = FALSE, expo_lag = 0L, ... )
dat 


graph  List with sparse matrices. 
timevar  Character scalar. Name of time variable 
depvar  Character scalar. Name of the dependent variable 
covariates  Character vector. Name(s) of the control variable(s). 
treat_thr  Either a numeric scalar or vector of length 
adopt_thr  Either a numeric scalar or vector of length 
expo_pcent  Logical scalar. When 
expo_lag  Integer scalar. Number of lags to consider when computing
exposure. 
...  Further arguments to be passed to 
In the case of netmatch_prepare
A data.frame
with the original data (covariates), plus the
following new variables: treat
, adopt
, exposure
.
A formula to be passed to netmatch
A numeric vector of length \(N_1\) (number of treated used in the matching process). Treatment effects on the treated at the individual level
The output from matchit
.
In Aral et al. (2009), the matching estimator is used as a response to the fact that the observed network is homophilous. Essentially, using exposure as a treatment indicator, which is known to be endogenous, we can apply the same principle of matching estimators in which, after controlling for characteristics (covariates), individuals from the treated group (exposed to some behavior) can be compared to individuals from the control group (not exposed to that behavior), as the only difference between the two is the exposure.
As pointed out in King & Nielsen (2015), it is suggested that, contrary to what Aral et al. (2009), the matching is not performed over propensity score since it is know that the later can increase imbalances in the data and thus obtaining exactly the opposed outcome that matching based estimators pursue.
A couple of good references for matching estimators are Imbens and Wooldridge (2009), and Sekhon (2008).
Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influencebased contagion from homophilydriven diffusion in dynamic networks. Proceedings of the National Academy of Sciences of the United States of America, 106(51), 21544–21549. doi: 10.1073/pnas.0908800106
Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5–86. doi: 10.1257/jel.47.1.5
King, G., & Nielsen, R. (2015). Why Propensity Scores Should Not Be Used for.
Sekhon, J. S. (2008). The NeymanRubin Model of Causal Inference and Estimation Via Matching Methods. The Oxford Handbook of Political Methodology. doi: 10.1093/oxfordhb/9780199286546.003.0011