MatchIt on Big Data - Horizontal data split on exact variables about matchit HOT 5 CLOSED

So funny you asked, both of these features are available in the development version of MatchIt on my GitHub. With nearest neighbor matching, when an argument to exact is specified, matching takes place seperately within each level of the exact matching variables, which will speed up execution. I have plans to allow this to be parallelized, too, but I haven't implemented that yet. Also, if you match separately within subgroups (i.e., using different calls to matchit()), you can now use a special rbind() method to bind the several match.data() outputs together into a single output for effect estimation. You do still have to assess balance within each matchit object separately, though, as these cannot be combined. You can be clever and use the rbind() output with cobalt if you retain the unmatched units in the match.data() calls.

To install the devlopment version on my GitHub, you can run devtools::install_github("ngreifer/MatchIt").

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Hi Noah, is this new feature implemented from MatchIt 4.2.0? I'm trying optimal pair matching with exact matching on some selected variables, and mahalanobis distance on other variables.

I can't get it to work with the full dataset which has about 500k rows (of which about 150k are in the treated group), i keep running out of memory.... Do i have to use a specific syntax for the matching to take place separately in each level of the exact matching variables? I'm currently using something like this:

match1 <- matchit(
  experiment_group ~ .,
  method = "optimal", 
  mahvars = ~ age + children + comedy + drama + ents + factual + learning + music + news + sport , 
  exact = c("gender", "acorn_category", "hf"),
  data = db
)

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Yes, that is a feature in 4.2.0, but only for nearest neighbor matching. Exact matching with optimal matching is handled by the optmatch package, which may not be able to handle such a large dataset. The ability to handle large datasets is an advantage of nearest neighbor over optimal matching. By setting verbose = TRUE with method = "nearest" you can also track the progress within each category of the exact variables. In general, nearest neighbor and optimal matching yield similar results, so you aren't losing anything by using nearest neighbor.

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thank you so much for such a prompt reply, i'll try nearest!

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