Forest Estimator Models

Random forest is a widely used algorithm in machine learning. Many empirical properties of random forest including stability and the ability of flexible adaptation to complicated forms have made random forest and its variants as popular and reliable choices in a lot of tasks. It is then a natural and crucial idea to extend tree based models for causal effect estimation such as causal tree to forest based ones. These works are pioneered by [Athey2018]. Similar to the case of machine learning, typically for causal effect estimation, forest estimator models have better performance than tree models while sharing equivalent interpretability and other advantages. Thus it is always recommended to try these estimator models first.

In YLearn, we currently cover three types of forest estimator models for causal effect estimation under the unconfoundness asssumption:

• Generalized Random Forest
  • Class Structures
• Causal Forest
  • Class Structures
• Ensemble of Causal Trees
  • Class Structures