There are different types of causal effects, and the choice of which type to focus on may depend on the research question, study design, and available data. Some common types of causal effects include:
Individual Treatment Effect (ITE): The effect of a specific treatment or intervention on an individual unit, such as a person, animal, or plant.
Average Treatment Effect (ATE): The effect of a treatment or intervention on a population, where the average is taken across all individuals or units in the population.
Conditional Average Treatment Effect (CATE): The effect of a treatment or intervention on a specific subgroup or condition, where the average is taken across only those individuals or units that meet certain criteria or have certain characteristics.
Direct Effect: The effect of a treatment or intervention on an outcome of interest, independent of any other variables or factors.
Indirect Effect: The effect of a treatment or intervention on an outcome of interest, mediated by one or more intermediate variables or factors.
Total Effect: The combined effect of a treatment or intervention on an outcome of interest, taking into account both direct and indirect effects.
These different types of causal effects can be estimated using various statistical methods, such as regression analysis, propensity score matching, and structural equation modeling, among others.
