The average treatment effect (ATE) is a measure used to compare treatments (or interventions) in randomized experiments, evaluation of policy interventions, and medical trials.
In a randomized trial (i.e., an experimental study), the average treatment effect can be estimated from a sample using a comparison in mean outcomes for treated and untreated units.
However, the ATE is generally understood as a causal parameter (i.e., an estimate or property of a population) that a researcher desires to know, defined without reference to the study design or estimation procedure.
The average treatment effect is under some conditions directly related to the partial dependence plot.
[1] 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.
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).
Indeed, units in both groups have identical distributions of covariates and potential outcomes.
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.
[2] If a sample is randomly constituted from a population, the sample ATE (abbreviated SATE) is also an estimate of the population ATE (abbreviated PATE).
[3] While an experiment ensures, in expectation, that potential outcomes (and all covariates) are equivalently distributed in the treatment and control groups, this is not the case in an observational study.
In an observational study, units are not assigned to treatment and control randomly, so their assignment to treatment may depend on unobserved or unobservable factors.
Observed factors can be statistically controlled (e.g., through regression or matching), but any estimate of the ATE could be confounded by unobservable factors that influenced which units received the treatment versus the control.
is the health status of the individual if they are not administered the drug under study and
In the general case, there is no reason to expect this effect to be constant across individuals.
The average treatment effect is given by and can be estimated (if a law of large numbers holds) where the summation occurs over all
among a large representative sample of the population, we could estimate the ATE simply by taking the average value of
This is the main problem faced by scientists in the evaluation of treatment effects and has triggered a large body of estimation techniques.
Depending on the data and its underlying circumstances, many methods can be used to estimate the ATE.
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.
A positive ATE, in this example, would suggest that the job policy increased the length of unemployment.
A negative ATE would suggest that the job policy decreased the length of unemployment.
An ATE estimate equal to zero would suggest that there was no advantage or disadvantage to providing the treatment in terms of the length of unemployment.
Determining whether an ATE estimate is distinguishable from zero (either positively or negatively) requires statistical inference.
Some parts of the population might be worse off with the treatment even if the mean effect is positive.
ATE requires a strong assumption known as the stable unit treatment value assumption (SUTVA) which requires the value of the potential outcome
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.
If the average treatment effects are different, SUTVA is violated.
[6][7] Recently, metalearning approaches have been developed that use arbitrary regression frameworks as base learners to infer the CATE.