# Identifying control group ex-post with no baseline- propensity score

We are trying to do an individual level impact evaluation using experiments. The intervention who's impact we are studying was at the town level. Unfortunately, we do not have a baseline control group (town or individual). To identify a control group we are conducting a PSM at the town level to identify a control group of towns using nearest neighbor matching. Once we have pairs of control-treatment towns, we will randomly select pairs to conduct the individual-level experiment in.

This is a simplified version of the code we are using to identify the pairs.

pscore treated \$pscore_controls,
pscore (mypscore) comsup numblo(5) level(0.01) blockid(myblockid)

psmatch2 treated, pscore( mypscore ) caliper (.001) noreplace neighbor(1)
**//nearest neighbor matching//**

gen pair = _id if _treated==0
replace pair = _n1 if _treated==1
**//identifies the matched treated
neighbor //**
bysort pair: egen paircount =count(pair)
tab paircount **//drop if !=2, if not 2 means no nearest neighbor was found in given caliper width- 119 of 157 treated towns matched//**


I have two questions:

1. Given that the intervention was at the village level but our outcome of interest is at the individual level- is the above-mentioned method a sound way to identify a control group?
2. The matching controls we are using are for 2010, the intervention was in 2015, the experiment will be in 2017/2018. We are assuming that since 2010 levels are informing our matching, they are pre-intervention and hence a sound way to identify a control group. Is there another way to identify a sound-control group?
• I am a bit confused. Will you have individual-level data from untreated towns? Or any untreated data from treated towns? – Dimitriy V. Masterov Oct 20 '17 at 18:11
• We will have individual-level data from both treated and untreated towns..but all this data will be post-intervention data. We have no baseline individual-level data. – Fatima Alvi Oct 23 '17 at 4:19