必需做Causal Inference的根源在于：
The “fundamental problem of causal inference” (Holland, 1986) is that we cannot expose an observational unit to treatment 1 and, at the same time, to treatment 0
其实这个问题，在工业界，譬如CTR，CVR估计等问题中，都是普遍存在的，但是一定程度上被很多人忽略了。
工业界常用场景：
1、AB testing
2、营销优化
3、模型去偏（Causal Based Method），Selection Bias
4、评估去偏，MNAR问题。Unbiased Offline Evaluation
关于：Conditional Effect[1][2]
关于：Counterfactual [3]
Refer
1、Probability and Causality. Conditional and Average Total Effects
2、causal inference与model的一些理解：
total Conditional effect可不用线性模型，直接积分掉？
https://www.zhihu.com/question/266812683/answer/895210894
