Transcription of teffects psmatch — Propensity-score matching
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teffects psmatch — Propensity-score matching
psmatch Propensity-score matchingDescriptionQuick startMenuSyntaxOptionsRemarks and examplesStored resultsMethods and formulasReferencesAlso seeDescriptionteffects psmatchestimates the average treatment effect (ATE) and average treatment effect onthe treated (ATET) from observational data by Propensity-score matching (PSM).PSMestimators imputethe missing potential outcome for each subject by using an average of the outcomes of similar subjectsthat receive the other treatment level. Similarity between subjects is based on estimated treatmentprobabilities, known as propensity scores. The treatment effect is computed by taking the average ofthe difference between the observed and potential outcomes for each psmatchaccepts a continuous, binary, count, fractional, or nonnegative [TE] teffects introor [TE] teffects intro advancedfor more information about estimatingtreatment effects from observational startATEoftreatonyestimated byPSMusing a logistic model fortreatonxand indicators for levelsof categorical variableateffects psmatch (y) (treat x )As above, but estimate theATET teffects psmatch (y) (treat x ), atetATEoftreatusing a heteroskedastic probit model for treatmentteffects psmatch (y) (treat x , hetprobit(x ))With 4 matches per observationteffects psmatch (y) (treat x ), nneighbor(4)MenuStatistics>Treatment effects>Continuous outc
probabilities, known as propensity scores. This type of matching is known as propensity-score matching (PSM). PSM does not need bias correction, because PSM matches on a single continuous covariate. In contrast, the nearest-neighbor matching estimator implemented in teffects nnmatch uses a bias-
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