| user_id | x1 | x2 | t1 | t2 | y | |
|---|---|---|---|---|---|---|
| 0 | 0 | 0.329533 | 1.899673 | 1 | 0 | 15.022716 |
| 1 | 1 | -0.393598 | -0.905984 | 0 | 1 | 5.393389 |
| 2 | 2 | -1.519506 | -1.196708 | 1 | 0 | 7.450544 |
| 3 | 3 | 0.593599 | 0.965585 | 1 | 0 | 13.244710 |
| 4 | 4 | -1.395265 | 0.968409 | 0 | 0 | 8.198693 |
MultiCausalData(df= user_id y x1 x2 t1 t2 0 0 15.022716 0.329533 1.899673 1 0 1 1 5.393389 -0.393598 -0.905984 0 1 2 2 7.450544 -1.519506 -1.196708 1 0 3 3 13.244710 0.593599 0.965585 1 0 4 4 8.198693 -1.395265 0.968409 0 0 .. ... ... ... ... .. .. 995 995 10.520261 0.067139 0.228348 0 0 996 996 7.888513 -0.399725 -0.149097 1 1 997 997 9.764297 -0.835026 0.730775 0 0 998 998 11.690357 -0.602435 -0.525958 1 0 999 999 9.104862 1.444279 -0.691310 0 1
[1000 rows x 6 columns], outcome_name='y', treatment_names=['t1', 't2'], confounders_names=['x1', 'x2'], user_id_name='user_id')