My general question is about controlling for a variable that is causally prior to the key quantity of interest in *some* observations, but not in others - when the two categories of observations are known ex ante. Specifically for our article, we are examining the effect of oil on democracy - and deciding whether to control for income. Income is probably causally posterior to oil in highly oil-dependent countries (and hence shouldn't be controlled for), but causally independent/prior to oil in others (and hence should be added as a control) - can we somehow "weigh" the control variable to account for this? Or, if the choice is between keeping or dropping it, and we choose the latter, should we argue that the degree of any induced omitted variable bias is less severe than the bias from post-treatment control variable would have been had we kept it..? Grateful, Stan p.s. Amelia is still kaput, crashing after 100 iterations with same error message.. **************************** Stanislav Markus Ph.D. Candidate Harvard University Department of Government e: smarkus(a)fas.harvard.edu t: 617.513.5407 -----Original Message----- From: gov2001-l-admin(a)fas.harvard.edu [mailto:gov2001-l-admin@fas.harvard.edu] On Behalf Of Kosuke Imai Sent: Tuesday, April 29, 2003 1:11 PM To: gov2001-l(a)fas.harvard.edu Subject: [gov2001-l] office hrs (fwd) I will have a regular office hour today from 4 to 6pm. Kosuke ---------- Forwarded message ---------- Date: Tue, 29 Apr 2003 11:58:30 -0400 (EDT) From: Yevgeniy Kirpichevsky <kirpich(a)fas.harvard.edu> To: Kosuke Imai <kimai(a)fas.harvard.edu> Subject: office hrs Kosuke, will you be in the office today @4? -y and p _______________________________________________ gov2001-l mailing list gov2001-l(a)fas.harvard.edu http://www.fas.harvard.edu/mailman/listinfo/gov2001-l