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Originating from early statistical analysis in the fields of agriculture and medicine, the term "treatment" is now applied, more generally, to other fields of natural and social science, especially [[psychology]], [[political science]], and [[economics]] such as, for example, the evaluation of the impact of public policies. The nature of a treatment or outcome is relatively unimportant in the estimation of the ATE—that is to say, calculation of the ATE requires that a treatment be applied to some units and not others, but the nature of that treatment (e.g., a pharmaceutical, an incentive payment, a political advertisement) is irrelevant to the definition and estimation of the ATE.

Originating from early statistical analysis in the fields of agriculture and medicine, the term "treatment" is now applied, more generally, to other fields of natural and social science, especially [[psychology]], [[political science]], and [[economics]] such as, for example, the evaluation of the impact of public policies. The nature of a treatment or outcome is relatively unimportant in the estimation of the ATE—that is to say, calculation of the ATE requires that a treatment be applied to some units and not others, but the nature of that treatment (e.g., a pharmaceutical, an incentive payment, a political advertisement) is irrelevant to the definition and estimation of the ATE.

”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。处理或结果的本质在评估平均处理效应时相对而言并不重要，也就是说，平均处理效应计算要求对某些单位进行处理，但不处理其他单位，但处理的本质(例如药物、奖励性支付、政治广告)与平均处理效应的定义和估计无关。

”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。处理或结果的具体内容在评估平均处理效应时相对而言并不重要，也就是说，平均处理效应估算要求对某些单位进行处理，但不处理其他单位，但处理具体内容(例如药物、奖励性支付、政治广告)与平均处理效应的定义和估计无关。

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>