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In Stata, IPW (Inverse Probability Weighting), psmatch
, and psmatch2
are all methods used for propensity score analysis, but they serve slightly different purposes. Here's how they compare:
1. IPW (Inverse Probability Weighting)
- Concept: Weights each observation by the inverse of the probability of receiving treatment, based on the estimated propensity score.
- Use case: Creates a pseudo-population where treatment assignment is independent of covariates.
- Implementation in Stata:
Estimate propensity scores usinglogit
orprobitÂ
logit treatment covariates
predict ps, pr - Strengths
- Uses the entire dataset (no need to drop unmatched units).
- Can handle high-dimensional covariates better than matching.
- Limitations:
- Sensitive to extreme weights (requires trimming).
2. psmatch
(Official Stata Module)
- Concept: Performs nearest neighbor matching based on estimated propensity scores.
- Use case: Compares treated and control units by selecting similar observations.
- Implementation:
3. psmatch2
(User-Written Module)
- Concept: An advanced matching method that extends
psmatch
with additional features. - Use case: Provides more flexible matching, including nearest neighbor, kernel, and radius matching.
- Installation:
Posted : 05/03/2025 1:01 pm