# Propensity Score

What are the various methods used for binary classification other than logistic regression?

What are the advantages of logistic reg. model in developing Propensity score w.r.t. other methods?

Actually I have been asked why? I backed it by saying its binary classification, so logit is perfect. Then I was asked why specifically logistic regression when there are various other binary classification methods?

• While you headed and tagged this question as related to propensity scores, your two questions look at odd to me: Binary classifiers are one thing, the use of propensity-scores with binary outcome is another topic. Are you asking whether logistic regression is better than, say, neural networks in deriving PSs? – chl Oct 20 '13 at 22:24
• Exactly, and I asked two questions....sorry for bad formatting. – user2035158 Oct 21 '13 at 14:12
• @chl it was my understanding that propensity scores can be used for any type of outcome, binary, continuous, or rates, events, etc. I had assumed that the Andy was asking about binary classifiers for the probability of receiving "treatment", i.e. how best to estimate propensity scores. – AdamO Sep 5 '14 at 15:01

• You're right that it is not universally the best approach. It is certainly defensible with a bare minimum of assumptions, such as reasonable sample size and number of propensity factors. All the caveats of developing a binary classifier apply. Hence I would look at boosting (and nearest neighbor, and LARS, and LASSO) as very specialized applications to be considered when $p > n$. – AdamO Sep 5 '14 at 15:29