Many applications of causal analysis call for assessing, retrospectively, the effect of withholding an action that has in fact been implemented. This counterfactual quantity, sometimes called "effect of treatment on the treated," (ETT) have been used to to evaluate educational programs, critic public...
平均干预效应（Average Treatment Effect，ATE）最终匹配的干预组和控制组在因变量上的平均差异 - 例如：ATE=456，“所有人上了大学之后，比所有人都没上大学平均多456元” 实验组的平均干预效应（Average Treatment Effect on the treated, ATT，或简称ATET)【我们只需调用和...
From sample average treatment effect to population average treatment effect on the treated: combining experimental with observational studies to estimate p... Randomized controlled trials (RCTs) can provide unbiased estimates of sample average treatment effects. However, a common concern is that RCTs ...
The work by Troxel et al. (2004) considers the problem of assessing sensitivity of estimates to violations of a missing at random condition, but in a parametric setting. Causal parameters such as the average treatment effect (ATE) and the average effect of treatment on the treated (ATT) are...
If an unobserved variable affects the treatment and affects the outcome we have an endogeneity problem, the result of which is that we cannot obtain accurate estimates of effects using conventional treatment effect estimators. For example, suppose we wish to know the effect on blood pressure of a...
Randomized controlled trials (RCTs) can provide unbiased estimates of sample average treatment effects. However, a common concern is that RCTs may fail to provide unbiased estimates of population average treatment effects. We derive the assumptions that are required to identify population average treatm...
Average treatment effect on the treated (ATT) at different grid resolutions with (a) and without (b) direct neighbors. Error bars depict Abadie-Imbens (AI) standard errors.Jan, BörnerKrisztina, KisKatosJorge, HargraveKon...
As a building block for the causal interpretation of estimates, we defineCATTe,ℓ, the cohort average treatment effects on the treated as the cohort-specific average difference in outcomes relative to never being treated. Our choice of a “building block” is governed by the counterfactual and ...
We revisit the classic semi‐parametric problem of inference on a low‐dimensional parameter θ0 in the presence of high‐dimensional nuisance parameters η0. We depart from the classical setting by allowing for η0 to be so high‐dimensional that the traditional assumptions (e.g. Donsker propert...
In experimental data, the treatment is randomized so that a difference between the average treated outcomes and the average nontreated outcomes estimates the average treatment effect (ATE). Suppose you want to estimate the ATE of a mother’s smoking on her baby’s birthweight. The ethical imposs...