Categories → Causal Inference , Study Design , Causal Effect
Conditional Average Treatment Effect (CATE) is a statistical concept used in causal inference to estimate the average causal effect of a treatment or intervention on an outcome variable of interest.
The CATE is a specific type of causal effect, which you might want to measure. CATE stands for "Conditional Average Treatment Effect" i.e. the average effect of the treatment or exposure on a sub-group. The validity of the estimate is conditional on being part of this subgroup. This is distinct from other causal effects such as the ATE, the Average Treatment Effect [on the entire population studied].
In particular, CATE refers to the difference in the expected value of the outcome variable between two groups of individuals who differ only in their treatment status, but are otherwise similar in terms of their observed characteristics or covariates. The conditional aspect of CATE refers to the fact that the treatment effect may vary across different subgroups of the population, depending on their covariate values.
To estimate CATE, one typically uses a statistical model that relates the outcome variable to the treatment status and the covariates, and then compares the predicted outcomes for the treated and untreated groups within each subgroup defined by the covariates. This can be done using techniques such as regression analysis, propensity score matching, or machine learning algorithms.
The CATE is a useful concept because it allows researchers to identify the specific subgroups of individuals who are most likely to benefit from a treatment or intervention, and to tailor the treatment to their individual needs. It also helps to identify potential heterogeneity in treatment effects, which can inform policy decisions and resource allocation.
If there are no effect modifiers, then CATE should be equal to ATE (see discussion here).
This video provides a great introduction: