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→因果推理的基本问题
The results we have seen up to this point would never be measured in practice. It is impossible, by definition, to observe the effect of more than one treatment on a subject over a specific time period. Joe cannot both take the pill and not take the pill at the same time. Therefore, the data would look something like this:
The results we have seen up to this point would never be measured in practice. It is impossible, by definition, to observe the effect of more than one treatment on a subject over a specific time period. Joe cannot both take the pill and not take the pill at the same time. Therefore, the data would look something like this:
到目前为止，我们所看到的结果永远无法在实践中衡量。根据定义，不可能在特定时间段内观察多种处理对受试者的影响。乔不能同时服用药物和不服用药物。因此，数据看起来像这样：
{| class="wikitable" align="center"
{| class="wikitable" align="center"
Assume that we have the following data:
Assume that we have the following data:
问号是无法观察到的反馈。因果推断的基本问题<ref name="holland:causal86"/>是不可能直接观察因果效应。然而，这并不使因果推断成为不可能。某些技术和假设可以克服基本问题。
问号是无法观察到的反馈。因果推断的基本问题<ref name="holland:causal86"/>是不可能直接观测因果效应。然而，这并不是说因果推断是不可能的。某些技术和假设可以克服基本问题。
假设我们有以下数据：
假设我们有以下数据：