Hi Gary,

 

Thanks for letting me know about the MatchingFrontier package, I’ll definitely explore it.

 

Sergio

 

From: Gary King [mailto:thegaryking@gmail.com]
Sent: 05 April 2017 19:00
To: Sergio Salis
Cc: cem@lists.gking.harvard.edu
Subject: Re: [cem] Few matched strata (and individuals within strata) after implementing cem

 

Sergio, the idea you describe in your first paragraph has been formalized
with this algorithm <http://projects.iq.harvard.edu/frontier/home>, and so
that's another option.  With CEM, you would decide how important it is to
get matches for each variable and coursen more for less important
variables.


Gary

---

http://gking.harvard.edu

617-500-7570

 

 

On Wed, Apr 5, 2017 at 12:32 PM, Sergio Salis <Sergio.Salis@natcen.ac.uk> wrote:

Hi Gary,

 

Thanks very much for your advice. I understand the idea is trying different coarsening strategies (among those which make sense) for each variable and see which one produces the lowest imbalance, measured by means of the Multivariate L1 distance (the univariate imbalances should also be looked at individually). Is this correct?

 

For variables like income and assets I guess it makes sense to use percentiles as there is no obvious value to create cut-off points. If so, shall I use

 

cem var1 var 2 …. income(P1 P2 …. Pn) , treatment(treated)

 

(where P1=value of the 1st percentile, P2=value of the 2nd percentile ….. Pn=value of the last percentile)?

 

(#10) will produce 10 equally sized bins but I am not sure whether equal size means equal base (e.g. bin 1 includes those with income between 1 to 10, bin 2 those with income 11 to 20, etc.) or equal frequencies (in which case a bin defines a percentile). I am also not sure what Sturge's rule and Scott’s algorithm are, I cannot find any description in the Stata help file.

 

Thanks again for your help< very much appreciated.

 

Sergio

 

From: Gary King [mailto:king@harvard.edu]
Sent: 05 April 2017 16:06
To: Sergio Salis
Cc: cem@lists.gking.harvard.edu
Subject: Re: [cem] Few matched strata (and individuals within strata) after implementing cem

 

Hi Sergio, you can adjust the coarsening rather than using the defaults in CEM.  more coarse bins will generate more observations.  you want to make the choices based on the substance of the variables, and which ones are more important to match finely on


Gary

--

Gary King - Albert J. Weatherhead III University Professor - Director, IQSS - Harvard University

 

On Wed, Apr 5, 2017 at 10:04 AM, Sergio Salis <Sergio.Salis@natcen.ac.uk> wrote:

 

Hi all,

 

I’m considering using the cem Stata programme to evaluate the impact of a welfare-to-work programme in the UK. However, I have never used cem before so I am trying to understand some basic issues before proceeding with the estimation.

 

The first thing I’d be interested in understanding is: How does one handle situations where after running cem the number of matched strata (and units within them) are very small?

 

Applying the cem algorithm to data from a previous impact evaluation I get:

 

Number of strata: 8883

Number of matched strata: 132

 

             0     1

      All                            8208  1584

  Matched                     179   141

Unmatched               8029  1443

 

If I calculate the ATT using cem matched data I get an impact estimate which is positive (around 5ppts; based on 320 obs only) while using psmatch2 on all data (i.e. not only those in cem matched strata; around 8,237 obs are used) with kernel weights I get an estimate of around -5.7ppts. This means I reach opposite conclusions about the impact of the programme of interest using cem and psmatch2.

 

I understand the cem-based estimates are based on better matched data (i.e. produce less biased estimates) compared to my psmatch2 estimate with kernel weights) but this comes at the expense of external validity: inference on the initial population is made based on a very small subset of data (estimates based on cem are not statistically significant while my original estimate was highly significant). Any advice about how one can handle situations of this type?

 

Many thanks,

Sergio

 

 

 

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