API: Interacting with YLearn
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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. |
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Find causal structures in observational data. |
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Express the causal structures and support other operations related to causal graph, e.g., add and delete edges to the graph. |
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Encode causations represented by the |
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Represent the probability distribution. |
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A highly flexible nonparametric estimator (Generalized Random Forest, GRF) model which supports both discrete and continuous treatment. The unconfoundedness condition is required. |
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A generalized random forest combined with the local centering technique (i.e. double machine learning framework). The unconfoundedness condition is required. |
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A causal forest as an ensemble of a bunch of |
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A model used for estimating the upper and lower bounds of the causal effects. This model does not need the unconfoundedness condition. |
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A class for estimating causal effect with decision tree. The unconfoundedness condition is required. |
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Instrumental variables with deep neural networks. Must provide the names of instrumental variables. |
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Nonparametric instrumental variables. Must provide the names of instrumental variables. |
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Double machine learning model for the estimation of CATE. The unconfoundedness condition is required. |
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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. |
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SLearner. The permuted version considers all possible treatment-control pairs. The unconfoundedness condition is required and the treatment must be discrete. |
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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. |
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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. |
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Effect score for measuring the performances of estimator models. The unconfoundedness condition is required. |
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A class for finding the optimal policy for maximizing the causal effect with the tree model. |
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An object used to interpret the estimated CATE using the decision tree model. |
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An object used to interpret the policy given by some |