summarize.estimator <- function(df, suffix='naive', coefname='x'){

    part <- df[,c('N',
                  'm',
                  'Bxy',
                  paste0('B',coefname,'y.est.',suffix),
                  paste0('B',coefname,'y.ci.lower.',suffix),
                  paste0('B',coefname,'y.ci.upper.',suffix),
                  'y_explained_variance',
                  'Bzx',
                  'Bzy',
                  'accuracy_imbalance_difference'
                  ),
               with=FALSE]


    true.in.ci <- as.integer((part$Bxy >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (part$Bxy <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]]))
    zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
    bias <- part[[paste0('B',coefname,'y')]] - part[[paste0('B',coefname,'y.est.',suffix)]]
    sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))

    part <- part[,':='(true.in.ci = true.in.ci,
                       zero.in.ci = zero.in.ci,
                       bias=bias,
                       sign.correct =sign.correct)]

    part.plot <- part[, .(p.true.in.ci = mean(true.in.ci),
                          mean.bias = mean(bias),
                          mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
                          var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
                          est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.975,na.rm=T),
                          est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.025,na.rm=T),
                          mean.ci.upper = mean(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],na.rm=T),
                          mean.ci.lower = mean(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],na.rm=T),
                          ci.upper.975 = quantile(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],0.975,na.rm=T),
                          ci.upper.025 = quantile(.SD[[paste0('B',coefname,'y.ci.upper.',suffix)]],0.025,na.rm=T),
                          ci.lower.975 = quantile(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],0.975,na.rm=T),
                          ci.lower.025 = quantile(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]],0.025,na.rm=T),
                          N.ci.is.NA = sum(is.na(.SD[[paste0('B',coefname,'y.ci.lower.',suffix)]])),
                          N.sims = .N,
                          p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
                          variable=coefname,
                          method=suffix
                          ),
                      by=c("N","m",'y_explained_variance','Bzx', 'Bzy', 'accuracy_imbalance_difference')
                      ]
    
    return(part.plot)
}