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I would like to use a greedy nearest neighbour method to do propensity score matching. Though I've little experience here, it seems that the distance measure used is generally a propensity score generated from a logistic regression. My question is: why logistic regression? Why not a random forest, SVM or another method? Is there some logic to suggest this would be unfruitful?

My plan is currently to use the MatchIt package in R and input my own distance measure calculated off the back of a random forest (you can input your own propensity score into the argument distance of the matchit function).

I'm deterred by the fact that modern propensity scoring packages such as PSAboot don't have built in facility to do this for nearest neighbour methods. They do use party and rpart but only to match using strata (additionally they only use single trees).

I'm intrigued that methods with (typically) greater predictive power than logistic regression do not appear to be harnessed to create a 'better' propensity score for one-to-one matching. Is there someone out there that can shed some light on this?

Linked question: Propensity Score

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    $\begingroup$ Greg Ridgeway has done a bit of work on using gradient boosting and then using those predictions for propensity score models. So your idea is not totally without precedent. $\endgroup$ – Andy W Aug 29 '14 at 17:24
  • $\begingroup$ Thanks @AndyW. His 2004 paper is v interesting. It even gives some R code at the bottom. $\endgroup$ – James Owers Aug 30 '14 at 17:23

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