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”处理”一词起源于农业和医药领域的早期统计分析，现在更广泛地用于自然和社会科学的其他领域，特别是心理学、政治科学和经济学，例如评价公共政策的影响。处理或结果在评估ATE时相对而言并不重要，也就是说，ATE的计算要求对某些单元进行处理，但不处理其他单元，但治疗的性质(例如药物、奖励性支付、政治广告)与处理的定义和估计无关。
”处理”一词起源于农业和医药领域的早期统计分析，现在更广泛地用于自然和社会科学的其他领域，特别是心理学、政治科学和经济学，例如评价公共政策的影响。处理或结果在评估ATE时相对而言并不重要，也就是说，ATE的计算要求对某些单元进行处理，但不处理其他单元，但治疗的性质(例如药物、奖励性支付、政治广告)与处理的定义和估计无关。
The expression "treatment effect" refers to the causal effect of a given treatment or intervention (for example, the administering of a drug) on an outcome variable of interest (for example, the health of the patient). In the [[Rubin causal model|Neyman-Rubin "potential outcomes framework"]] of [[causality]] a treatment effect is defined for each individual unit in terms of two "potential outcomes." Each unit has one outcome that would manifest if the unit were exposed to the treatment and another outcome that would manifest if the unit were exposed to the control. The "treatment effect" is the difference between these two potential outcomes. However, this individual-level treatment effect is unobservable because individual units can only receive the treatment or the control, but not both. [[Random assignment]] to treatment ensures that units assigned to the treatment and units assigned to the control are identical (over a large number of iterations of the experiment). Indeed, units in both groups have identical [[Probability distribution|distributions]] of [[covariate]]s and potential outcomes. Thus the average outcome among the treatment units serves as a [[Counterfactual conditional|counterfactual]] for the average outcome among the control units. The differences between these two averages is the ATE, which is an estimate of the [[central tendency]] of the distribution of unobservable individual-level treatment effects.<ref>{{cite journal |last=Holland |first=Paul W. |year=1986 |title=Statistics and Causal Inference |journal=[[Journal of the American Statistical Association|J. Amer. Statist. Assoc.]] |volume=81 |issue=396 |pages=945–960 |jstor=2289064 |doi=10.1080/01621459.1986.10478354}}</ref> If a sample is randomly constituted from a population, the sample ATE (abbreviated SATE) is also an estimate of the population ATE (abbreviated PATE).<ref>{{cite journal |last=Imai |first=Kosuke |first2=Gary |last2=King |first3=Elizabeth A. |last3=Stuart |year=2008 |title=Misunderstandings Between Experimentalists and Observationalists About Causal Inference |journal=[[Journal of the Royal Statistical Society, Series A|J. R. Stat. Soc. Ser. A]] |volume=171 |issue=2 |pages=481–502 |doi=10.1111/j.1467-985X.2007.00527.x |url=http://nrs.harvard.edu/urn-3:HUL.InstRepos:4142695 }}</ref>
The expression "treatment effect" refers to the causal effect of a given treatment or intervention (for example, the administering of a drug) on an outcome variable of interest (for example, the health of the patient). In the [[Rubin causal model|Neyman-Rubin "potential outcomes framework"]] of [[causality]] a treatment effect is defined for each individual unit in terms of two "potential outcomes." Each unit has one outcome that would manifest if the unit were exposed to the treatment and another outcome that would manifest if the unit were exposed to the control. The "treatment effect" is the difference between these two potential outcomes. However, this individual-level treatment effect is unobservable because individual units can only receive the treatment or the control, but not both. [[Random assignment]] to treatment ensures that units assigned to the treatment and units assigned to the control are identical (over a large number of iterations of the experiment). Indeed, units in both groups have identical [[Probability distribution|distributions]] of [[covariate]]s and potential outcomes. Thus the average outcome among the treatment units serves as a [[Counterfactual conditional|counterfactual]] for the average outcome among the control units. The differences between these two averages is the ATE, which is an estimate of the [[central tendency]] of the distribution of unobservable individual-level treatment effects.<ref>{{cite journal |last=Holland |first=Paul W. |year=1986 |title=Statistics and Causal Inference |journal=[[Journal of the American Statistical Association|J. Amer. Statist. Assoc.]] |volume=81 |issue=396 |pages=945–960 |jstor=2289064 |doi=10.1080/01621459.1986.10478354}}</ref> If a sample is randomly constituted from a population, the sample ATE (abbreviated SATE) is also an estimate of the population ATE (abbreviated PATE).<ref>{{cite journal |last=Imai |first=Kosuke |first2=Gary |last2=King |first3=Elizabeth A. |last3=Stuart |year=2008 |title=Misunderstandings Between Experimentalists and Observationalists About Causal Inference |journal=[[Journal of the Royal Statistical Society, Series A|J. R. Stat. Soc. Ser. A]] |volume=171 |issue=2 |pages=481–502 |doi=10.1111/j.1467-985X.2007.00527.x |url=http://nrs.harvard.edu/urn-3:HUL.InstRepos:4142695 }}</ref>
”处理效果”一词是指某一特定处理或干预(例如，给予某种药物)对有关结果变量(例如，病人的健康)的因果影响。在因果关系的 Neyman-Rubin“潜在结果框架”中，处理效果被定义为每个个体单元的两个“潜在结果”，如果该个体单元给与处理，就会显现一种结果; 如果该个体单元不给予处理，就会显现出另一个结果。“处理效果”是这两种潜在结果之间的差异。然而，这种个体水平的处理效果是不可观察的，因为个体单位只能接受处理或不接受处理，但不能同时两者。随机分配给处理确保分配给处理的单元和分配给控制的单元是相同的(经过大量的迭代实验)。事实上，两组中的单位在协变量和潜在结果上的分布是相同的。因此，处理单元之间的平均结果是控制单元的平均结果的反事实。这两个平均值之间的差异是 ATE，这是不可观测到的个人水平的处理效果的中心趋势的估计。如果样本是从总体中随机构成的，那么样本 ATE (缩写为SQTE)也是总体 ATE (缩写为 PATE)的估计值。