Comments (2)
Thanks for the suggestion. This is going to be implemented in a major update to MatchIt
that hopefully will occur later this year or early next year. Note, though, that standard error estimation for this method is not well understood outside of the context of matching imputation as described by Abadie and Imbens (2006) and implemented in the Matching
package. Matching
provides an accurate standard error estimator for the ATE when matching with replacement, so for now, I recommend using it instead of MatchIt
if you are set on using this method. Otherwise, subclassification, optimal full matching, and generalized full matching can be good alternatives. They don't have a match.matrix
because they don't involve create pairs; rather, they form subclasses.
from matchit.
from matchit.
Related Issues (20)
- Data needs to be randomized before sending it to matchit. Is this desired behaviour? HOT 7
- Weights for t test, chi square test, and effective sample size of subgroups HOT 5
- Match selected samples from both control and treatment cohort HOT 4
- Can glm.nb be used after running matching with Matchit? HOT 1
- Large Dataset with MatchIT HOT 13
- Issue with using exact statement within optimal matching (using MatchIt) HOT 14
- Matching strategy for clusters that don't include both treatment groups - multilevel matching HOT 3
- Issue regarding the unit.id argument HOT 4
- ATT estimate in survival analysis HOT 2
- best way to estimate interactions? HOT 7
- dependency on optmatch HOT 3
- Error installing "MatchIt" HOT 1
- Cardinality matching fails when there is only one covariate
- Running tests fails: Error: could not find function "avg_comparisons", "boot.ci" HOT 3
- Obtaining odds ratios vs risk ratios using avg_comparisons() HOT 1
- Predetermined subsample size HOT 1
- Imputed Data for Matching HOT 2
- Cross-Validation for logistic regression propensity score predictions HOT 3
- Possibility of kernel matching HOT 4
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from matchit.