Hello,
I want to analyse the ATT of an intervention using PSM. My treatment has three arms: the control group, treatment 1 receiving only treatment 1, and treatment 2 receiving both treatment 1 and 2. In a first step, I simply compared receiving any treatment to being in the control group using the following commands:
Where treated is a binary variable indicating if any treatment was received, cov1-3 are the covariates I want to use for the matching, and dep1-3 are my different outcome variables of interest.
Now I want to look at the effects of the two treatments separately. As far as I have understood, that requires three different comparisons: Control vs T1, Control vs T2, T1 vs T2. I already estimated the probabilities of being in the different treatments using mlogit and estimated the pscores:
However, I struggle to find how to estimate the different effects using psmatch2. Using teffects with psmatch also does not seem to be an option with multiple treatments.
I am using Stata18.
Thank you!
I want to analyse the ATT of an intervention using PSM. My treatment has three arms: the control group, treatment 1 receiving only treatment 1, and treatment 2 receiving both treatment 1 and 2. In a first step, I simply compared receiving any treatment to being in the control group using the following commands:
Code:
probit treated cov1 cov2 cov3 predict pscore_treated foreach y of varlist dep1 dep2 dep3 { psmatch2 treated, outcome(`y') pscore(pscore_treated) kernel common eststo nn_treated_`y' esttab nn_pooled_dep1 nn_pooled_dep2 nn_pooled_dep3, keep(_treated) }
Now I want to look at the effects of the two treatments separately. As far as I have understood, that requires three different comparisons: Control vs T1, Control vs T2, T1 vs T2. I already estimated the probabilities of being in the different treatments using mlogit and estimated the pscores:
Code:
mlogit treatments cov1 cov2 cov3 predict pscore_control pscore_t1 pscore_t2
I am using Stata18.
Thank you!
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