I am trying to create a macro to loop through all possible pairwise interactions in a regression analysis. I am using the Imbens and Rubin approach to estimating a propensity score. I have selected my main effects and now I want to test for possible pairwise interactions to retain in the regression model. I need to include a number of forced variables (currently written for 12) and then test one pairwise interaction at a time.    All but one (momage) of my variables are dichotomous (dummy) vars, but I want the macro to be able to handle either dichotomous or quantitative variables. e.g. iterate through all possible interactions and I can ignore the ones I don't need.    In the attached code, I have tried to pass my main effects into an array, and then use a do loop to generate the interaction variables from the array. Those array variables should then pass into the macro and repeat until all pairwise interactions are tested.    I get two errors with this code 1)  NOTE: The SAS System stopped processing this step because of errors. NOTE: SAS set option OBS=0 and will continue to check statements. This may cause NOTE: No observations in data set. WARNING: The data set WORK.WNHEST may be incomplete. When this step was stopped there were 0 observations and 161 variables. 2)  MLOGIC(PAIRWISEINT): Ending execution. ERROR 117-185: There were 2 unclosed DO blocks.   I have read the information about using the BY statement with a transformed dataset (https://blogs.sas.com/content/iml/2017/02/13/run-1000-regressions.html) but I don't see how to get the interactions fed into the regressions this way.    I thought about using %DO within the macro but the order didn't make sense to me that way, I think it should be array -> generate interaction terms -> terms go into macro input and then used in regression.   If there are any recommendations on additional reading I welcome those too. Thanks in advance! SAS Software 9.2 (TS2M3) Linux 2.6.18-402.el5PAE (LINUX) platform   data test; input msdp momage nomarnopn nomarpn marnopn momedu1 momedu3 momedu4 momedu5 bmi_un bmi_ov bmi_ob moborn; CARDS; 1 19 1 0 0 1 0 0 0 0 0 0 1 1 20 0 1 0 0 0 0 0 1 0 0 0 1 21 0 0 1 0 0 1 0 0 1 0 1 1 23 0 1 0 0 0 0 1 0 0 1 0 1 25 1 0 0 0 0 1 0 0 1 0 1 0 27 0 1 0 0 1 0 0 1 0 0 0 0 29 0 0 0 0 0 0 0 0 0 0 1 0 31 0 1 0 1 0 0 0 1 0 0 0 0 33 0 0 1 0 1 0 0 0 1 0 1 0 35 0 0 0 0 0 0 0 0 0 1 0 ; RUN;   OPTIONS mprint symbolgen mlogic spool; %MACRO pairwiseint(data=, x1=, x2=, x3=, x4=, x5=, x6=, x7=, x8=, x9=, x10=, x11=, x12=, xi= , xj= , nummainefx=, outcome=, penter=, pkeep=); proc logistic data=&data DESCENDING; class &x2 &x3 &x4 &x5 &x6 &x7 &x8 &x9 &x10 &x11 &x12 / PARAM=REF; model &outcome(event='1') = &x1 &x2 &x3 &x4 &x5 &x6 &x7 &x8 &x9 &x10 &x11 &x12 &xi*&xj /include=&nummainefx selection=stepwise slentry=&penter slstay=&pkeep details lackfit; %MEND pairwiseint; *set arrays for macro variables; *keep xi the same; *the number is the number of main effects in the model; *then define main effect variables; data wnhest; set wnhest; array main {12} momage nomarnopn nomarpn marnopn momedu1 momedu3 momedu4 momedu5 bmi_un bmi_ov bmi_ob moborn; DO i = 1 to 12 by 1; Do j = i to 12 by 1; xi=main[i]; xj=main[j]; %pairwiseint(data=wnhest, x1=momage, x2=nomarnopn, x3=nomarpn, x4=marnopn, x5=momedu1, x6=momedu3, x7=momedu4, x8=momedu5, x9=bmi_un, x10=bmi_ov, x11=bmi_ob, x12=moborn, xi=xi, xj=xj, nummainefx=12, outcome=msdp, penter=.35, pkeep=.95); end; end; run; ... View more