# results ## 3 exps ![[_forest-main-3exp-att.png]] ![[_forest-main-3exp-itt.png]] ![[_thresholds-main-3exp.png]] ## 4 exps ![[_forest-main-4exp-att.png]] ![[_forest-main-4exp-itt.png]] ![[_thresholds-main-4exp.png]] ```r # 3 experiments > (df_meta_itt_results <- rbindlist(meta_itt_results)) threshold estimate se low high pval 1: 40 -0.0001478677 0.0001153368 -0.0003739236 7.818822e-05 0.1998246 2: 45 -0.0001876058 0.0001214700 -0.0004256826 5.047104e-05 0.1224765 3: 50 -0.0002113267 0.0001290397 -0.0004642399 4.158648e-05 0.1014869 4: 55 -0.0001741698 0.0001530019 -0.0004740480 1.257085e-04 0.2549743 5: 60 -0.0001686277 0.0001689476 -0.0004997589 1.625036e-04 0.3182278 6: 65 -0.0001075043 0.0001938334 -0.0004874109 2.724022e-04 0.5791531 7: 70 -0.0001926394 0.0002216320 -0.0006270301 2.417512e-04 0.3847454 8: 75 -0.0001661042 0.0002338831 -0.0006245065 2.922982e-04 0.4775791 9: 80 -0.0001765096 0.0002498652 -0.0006662364 3.132172e-04 0.4799274 > (df_meta_att_results <- rbindlist(meta_att_results)) threshold estimate se low high pval 1: 40 -0.0002380702 0.0001869588 -0.0006045026 1.283622e-04 0.2028820 2: 45 -0.0003017619 0.0001966789 -0.0006872455 8.372176e-05 0.1249591 3: 50 -0.0003360696 0.0002084390 -0.0007446024 7.246328e-05 0.1068931 4: 55 -0.0002751357 0.0002487619 -0.0007627000 2.124286e-04 0.2687177 5: 60 -0.0002591731 0.0002734994 -0.0007952221 2.768759e-04 0.3433236 6: 65 -0.0001622786 0.0003141745 -0.0007780493 4.534920e-04 0.6054886 7: 70 -0.0002915616 0.0003603177 -0.0009977714 4.146482e-04 0.4184122 8: 75 -0.0002440960 0.0003804851 -0.0009898332 5.016411e-04 0.5211726 9: 80 -0.0002488782 0.0004065834 -0.0010457670 5.480106e-04 0.5404577 # 4 experiments > (df_meta_itt_results <- rbindlist(meta_itt_results)) threshold estimate se low high pval 1: 40 -0.0001060092 8.867185e-05 -0.0002798028 6.778443e-05 0.23188291 2: 45 -0.0001292892 9.292250e-05 -0.0003114139 5.283559e-05 0.16411460 3: 50 -0.0001501199 9.792128e-05 -0.0003420421 4.180229e-05 0.12525931 4: 55 -0.0001600407 1.148127e-04 -0.0003850695 6.498804e-05 0.16333912 5: 60 -0.0001976265 1.287986e-04 -0.0004500672 5.481415e-05 0.12493526 6: 65 -0.0002149198 1.464581e-04 -0.0005019723 7.213276e-05 0.14225388 7: 70 -0.0002927110 1.672898e-04 -0.0006205930 3.517099e-05 0.08016593 8: 75 -0.0003251855 1.771309e-04 -0.0006723556 2.198465e-05 0.06638003 9: 80 -0.0003706866 1.877802e-04 -0.0007387290 -2.644191e-06 0.04837659 > (df_meta_att_results <- rbindlist(meta_att_results)) threshold estimate se low high pval 1: 40 -0.0001706875 0.0001436316 -0.0004522003 1.108252e-04 0.23468756 2: 45 -0.0002080781 0.0001504162 -0.0005028885 8.673221e-05 0.16655783 3: 50 -0.0002397182 0.0001582898 -0.0005499606 7.052408e-05 0.12991777 4: 55 -0.0002550897 0.0001862806 -0.0006201931 1.100136e-04 0.17087923 5: 60 -0.0003118564 0.0002084658 -0.0007204418 9.672910e-05 0.13466425 6: 65 -0.0003414439 0.0002372147 -0.0008063762 1.234884e-04 0.15004078 7: 70 -0.0004630752 0.0002714200 -0.0009950487 6.889833e-05 0.08798573 8: 75 -0.0005136822 0.0002874999 -0.0010771716 4.980722e-05 0.07398253 9: 80 -0.0005811587 0.0003048117 -0.0011785787 1.626133e-05 0.05657043 ``` # paths - exp1: (# tweets with bad domains / all retweets) - campaign0003/data-v13-prep-meta-processed - exp2: (# tweets with bad domains / all retweets) - campaign0004/data-v07-prep-meta-processed - exp3: (# tweets with bad domains / all retweets) - campaign0005/data-v03-prep-meta-processed - exp4: (# tweets with bad domains / all retweets) - campaign0006/data-v02-prep-meta-processed