API: Interacting with YLearn

All-in-one API

Class Name	Description
Why	An API which encapsulates almost everything in YLearn, such as identifying causal effects and scoring a trained estimator model. It provides to users a simple and efficient way to use YLearn.

Causal Structures Discovery

Class Name	Description
CausalDiscovery	Find causal structures in observational data.

Causal Model

Class Name	Description
CausalGraph	Express the causal structures and support other operations related to causal graph, e.g., add and delete edges to the graph.
CausalModel	Encode causations represented by the CausalGraph. Mainly support causal effect identification, e.g., backdoor adjustment.
Prob	Represent the probability distribution.

Estimator Models

Class Name	Description
GRForest	A highly flexible nonparametric estimator (Generalized Random Forest, GRF) model which supports both discrete and continuous treatment. The unconfoundedness condition is required.
CausalForest	A generalized random forest combined with the local centering technique (i.e. double machine learning framework). The unconfoundedness condition is required.
CTCausalForest	A causal forest as an ensemble of a bunch of CausalTree. Similar to the CausalTree, the treatment should be binary. The unconfoundedness condition is required.
ApproxBound	A model used for estimating the upper and lower bounds of the causal effects. This model does not need the unconfoundedness condition.
CausalTree	A class for estimating causal effect with decision tree. The unconfoundedness condition is required.
DeepIV	Instrumental variables with deep neural networks. Must provide the names of instrumental variables.
NP2SLS	Nonparametric instrumental variables. Must provide the names of instrumental variables.
DoubleML	Double machine learning model for the estimation of CATE. The unconfoundedness condition is required.
DoublyRobust and PermutedDoublyRobust	Doubly robust method for the estimation of CATE. The permuted version considers all possible treatment-control pairs. The unconfoundedness condition is required and the treatment must be discrete.
SLearner and PermutedSLearner	SLearner. The permuted version considers all possible treatment-control pairs. The unconfoundedness condition is required and the treatment must be discrete.
TLearner and PermutedTLearner	TLearner with multiple machine learning models. The permuted version considers all possible treatment-control pairs. The unconfoundedness condition is required and the treatment must be discrete.
XLearner and PermutedXLearner	XLearner with multiple machine learning models. The permuted version considers all possible treatment-control pairs. The unconfoundedness condition is required and the treatment must be discrete.
RLoss	Effect score for measuring the performances of estimator models. The unconfoundedness condition is required.

Policy

Class Name	Description
PolicyTree	A class for finding the optimal policy for maximizing the causal effect with the tree model.

Interpreter

Class Name	Description
CEInterpreter	An object used to interpret the estimated CATE using the decision tree model.
PolicyInterpreter	An object used to interpret the policy given by some PolicyModel.