I'm trying to use the Match() function from the Matching package in R to do a propensity score analysis.
My outcome of interest is a binary variable (0/1). My treatment is also a binary variable (0/1). In addition, I have a number of other variables that I want to control for in this analysis.
First, I fit a logistic regression to define a propensity score for the treatment:
glm1 = glm(Treatment ~ variable1 + variable2 + variable3 + ..., data=dataset, family="binomial")
Then, I used the Match function to estimate the average treatment effect on the treated:
rr1 = Match(Y = Outcome, Tr = Treatment, X = glm1$fitted)
Finally, I called for a summary:
My question is how to interpret the output. I get:
Estimate... -0.349, AI SE... 0.124, T-stat... -2.827, p.val... 0.005
What does this mean? In particular, what is Estimate? The documentation says it's "The estimated average causal effect." But what are the units? Can I interpret this to mean that the treatment reduced the outcome by a relative 35%? Or by an absolute 0.35? Or do I need to exponentiate?
Any help on the interpretation would be much appreciated!