更改

Depending on the data and its underlying circumstances, many methods can be used to estimate the ATE. The most common ones are:

Depending on the data and its underlying circumstances, many methods can be used to estimate the ATE. The most common ones are:

== An example ==

== An example ==

Consider an example where all units are unemployed individuals, and some experience a policy intervention (the treatment group), while others do not (the control group). The causal effect of interest is the impact a job search monitoring policy (the treatment) has on the length of an unemployment spell: On average, how much shorter would one's unemployment be if they experienced the intervention? The ATE, in this case, is the difference in expected values (means) of the treatment and control groups' length of unemployment.

Consider an example where all units are unemployed individuals, and some experience a policy intervention (the treatment group), while others do not (the control group). The causal effect of interest is the impact a job search monitoring policy (the treatment) has on the length of an unemployment spell: On average, how much shorter would one's unemployment be if they experienced the intervention? The ATE, in this case, is the difference in expected values (means) of the treatment and control groups' length of unemployment.

Because the ATE is an estimate of the average effect of the treatment, a positive or negative ATE does not indicate that any particular individual would benefit or be harmed by the treatment. Thus the average treatment effect neglects the distribution of the treatment effect. Some parts of the population might be worse off with the treatment even if the mean effect is positive.

Because the ATE is an estimate of the average effect of the treatment, a positive or negative ATE does not indicate that any particular individual would benefit or be harmed by the treatment. Thus the average treatment effect neglects the distribution of the treatment effect. Some parts of the population might be worse off with the treatment even if the mean effect is positive.

因为 ATE 是对治疗的平均效果的估计，一个正的或负的 ATE 并不表明任何特定的个人会受益或受到治疗的伤害。因此，平均治疗效果忽略了治疗效果的分布。即使平均效应是正面的，一部分人口可能会因为这种治疗而变得更糟。

因为 ATE 是对治疗的平均效果的估计，一个正的或负的 ATE 并不表明任何特定的个人会受益或受到治疗的伤害。因此，平均治疗效果忽略了治疗效果的分布。即使平均效应是正面的，一部分人口可能会因为这种治疗而变得更糟。

Some researchers call a treatment effect "heterogenous" if it affects different individuals differently (heterogeneously).  For example, perhaps the above treatment of a job search monitoring policy affected men and women differently, or people who live in different states differently.

Some researchers call a treatment effect "heterogenous" if it affects different individuals differently (heterogeneously).  For example, perhaps the above treatment of a job search monitoring policy affected men and women differently, or people who live in different states differently.

一些研究人员称治疗效果“异质性”，如果它影响不同的个人(异质性)。例如，上面提到的求职监控政策对男性和女性的影响是不同的，或者对生活在不同州的人的影响是不同的。

一些研究人员称治疗效果“异质性”，如果它影响不同的个人(异质性)。例如，上面提到的求职监控政策对男性和女性的影响是不同的，或者对生活在不同州的人的影响是不同的。

One way to look for heterogeneous treatment effects is to divide the study data into subgroups (e.g., men and women, or by state), and see if the average treatment effects are different by subgroup.  A per-subgroup ATE is called a "conditional average treatment effect" (CATE), i.e. the ATE conditioned on membership in the subgroup.

One way to look for heterogeneous treatment effects is to divide the study data into subgroups (e.g., men and women, or by state), and see if the average treatment effects are different by subgroup.  A per-subgroup ATE is called a "conditional average treatment effect" (CATE), i.e. the ATE conditioned on membership in the subgroup.

寻找异质治疗效果的一种方法是将研究数据分为子组(例如，男性和女性，或按州) ，看看平均治疗效果是否因子组而异。每个子组的 ATE 被称为“条件平均治疗效应”(CATE) ，也就是说，每个子组的 ATE 被称为条件平均治疗效应。自动选举委员会的条件是成员在该小组。

寻找异质治疗效果的一种方法是将研究数据分为子组(例如，男性和女性，或按州) ，看看平均治疗效果是否因子组而异。每个子组的 ATE 被称为“条件平均治疗效应”(CATE) ，也就是说，每个子组的 ATE 被称为条件平均治疗效应。自动选举委员会的条件是成员在该小组。

A challenge with this approach is that each subgroup may have substantially less data than the study as a whole, so if the study has been powered to detect the main effects without subgroup analysis, there may not be enough data to properly judge the effects on subgroups.

A challenge with this approach is that each subgroup may have substantially less data than the study as a whole, so if the study has been powered to detect the main effects without subgroup analysis, there may not be enough data to properly judge the effects on subgroups.

这种方法的一个挑战是，每个分组的数据可能比整个研究少得多，所以如果这项研究在没有进行分组分析的情况下就能检测出主要的影响，那么就可能没有足够的数据来正确判断对分组的影响。

这种方法的一个挑战是，每个分组的数据可能比整个研究少得多，所以如果这项研究在没有进行分组分析的情况下就能检测出主要的影响，那么就可能没有足够的数据来正确判断对分组的影响。