RCT scenario
Result
CausalData(df=(20000, 11), treatment='d', outcome='y', confounders=['x1', 'x2', 'age', 'cnt_trans', 'platform_Android', 'platform_iOS', 'invited_friend', 'y_pre'], user_id='user_id')
Result
| y | d | x1 | x2 | y_pre | m | m_obs | tau_link | g0 | g1 | cate | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 1.0 | 0.304717 | -1.039984 | 0.839632 | 0.5 | 0.5 | 0.204794 | 0.204632 | 0.239978 | 0.035346 |
| 1 | 0.0 | 0.0 | 0.750451 | 0.940565 | -0.520782 | 0.5 | 0.5 | 0.204794 | 0.061919 | 0.074937 | 0.013018 |
| 2 | 0.0 | 1.0 | -1.951035 | -1.302180 | 0.546401 | 0.5 | 0.5 | 0.204794 | 0.160998 | 0.190614 | 0.029616 |
| 3 | 0.0 | 1.0 | 0.127840 | -0.316243 | 0.269294 | 0.5 | 0.5 | 0.204794 | 0.126980 | 0.151468 | 0.024488 |
| 4 | 0.0 | 1.0 | -0.016801 | -0.853044 | 0.632883 | 0.5 | 0.5 | 0.204794 | 0.173024 | 0.204314 | 0.031289 |
Result
estimand coefficient p_val lower_ci upper_ci relative_diff_%
0 ATE 0.02404 6.560385e-07 0.01457 0.03351 19.547229
is_significant
0 True