Re: [cem] Few matched strata (and individuals within strata) after implementing cem

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(a)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 <http://iq.harvard.edu/> - Harvard University GaryKing.org - King(a)Harvard.edu - @KingGary <https://twitter.com/kinggary> - 617-500-7570 <(617)%20500-7570> - Assistant &lt;king-assist(a)iq.harvard.edu&gt;du>: 617-495-9271 <(617)%20495-9271> On Wed, Apr 5, 2017 at 10:04 AM, Sergio Salis &lt;Sergio.Salis(a)natcen.ac.uk&gt; 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 NatCen Social Research 35 Northampton Square London EC1V 0AX 020 7250 1866 Visit our website. www.natcen.ac.uk Read our latest blog. http://www.natcen.ac.uk/blog Follow us. @NatCen <https://twitter.com/natcen> Email us. info(a)natcen.ac.uk NatCen Social Research is certificated to ISO/IEC 27001:2013 for Information Security Management Systems and to ISO 20252:2012, the international standard for market, opinion and social research. Company limited by guarantee. Registered in England No. 4392418. Charity registered in England and Wales (1091768) and in Scotland (SC038454). Confidentiality: The information in this email and any attachments are confidential and may include some that is legally privileged. It must not be disclosed to or used by persons other than the intended recipient. If received in error, please notify us immediately and then delete this document. Content: Any views or opinions expressed do not necessarily represent those of NatCen Social Research. Please note the content of this e-mail may be intercepted, monitored or recorded for compliance purposes. Sensitive personal data should not normally be transmitted by e-mail. Copyright: Copyright in this e-mail and any attachments created by NatCen Social Research belong to NatCen Social Research unless otherwise stated. Care: NatCen Social Research shall not be liable to the recipient or any third party for any loss or damage howsoever arising from this e-mail and/or its content, including loss or damage caused by virus. It is the responsibility of the recipient to ensure the opening or use of this message and any attachments shall not adversely affect systems or data.