. cate aipw (assets \$catecovars) (e401k), rseed(12345671) ///
>         controls(\$controls) group(incomecat) nolog
{\smallskip}
Conditional average treatment effects     Number of observations       = 9,913
Estimator:       Augmented IPW            Number of folds in cross-fit =    10
Outcome model:   Linear lasso             Number of outcome controls   =    47
Treatment model: Logit lasso              Number of treatment controls =    47
CATE model:      Random forest            Number of CATE variables     =    17
{\smallskip}
\HLI{13}{\TOPT}\HLI{64}
             {\VBAR}               Robust
      assets {\VBAR} Coefficient  std. err.      z    P>|z|     [95\% conf. interval]
\HLI{13}{\PLUS}\HLI{64}
GATE         {\VBAR}
   incomecat {\VBAR}
          0  {\VBAR}   4295.829   992.7063     4.33   0.000      2350.16    6241.497
          1  {\VBAR}   628.2236   1690.636     0.37   0.710    -2685.362    3941.809
          2  {\VBAR}    5562.85   1310.006     4.25   0.000     2995.284    8130.415
          3  {\VBAR}   9058.087   2276.042     3.98   0.000     4597.125    13519.05
          4  {\VBAR}   21275.42   4716.757     4.51   0.000     12030.74    30520.09
\HLI{13}{\PLUS}\HLI{64}
ATE          {\VBAR}
       e401k {\VBAR}
  (Eligible  {\VBAR}
         vs  {\VBAR}
Not elig..)  {\VBAR}   8164.364   1151.125     7.09   0.000       5908.2    10420.53
\HLI{13}{\PLUS}\HLI{64}
POmean       {\VBAR}
       e401k {\VBAR}
Not eligi..  {\VBAR}   13910.87   842.0945    16.52   0.000     12260.39    15561.34
\HLI{13}{\BOTT}\HLI{64}
