YLearn
latest
用户指南
因果模型:表示因果结构
估计器模型:估计因果效应
因果发现:探索数据中的因果结构
策略:选择最佳选项
解释器:解释因果效应
Why: 一个一体化的因果学习API
参考文献
YLearn
索引
在 GitHub 上编辑
索引
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_comp_transormer() (ylearn.estimator_model.meta_learner.SLearner 方法)
_comp_transormer() (ylearn.estimator_model.meta_learner.TLearner 方法)
_comp_transormer() (ylearn.estimator_model.meta_learner.XLearner 方法)
A
add_edge() (ylearn.causal_model.graph.CausalGraph 方法)
add_edges_from() (ylearn.causal_model.graph.CausalGraph 方法)
add_nodes() (ylearn.causal_model.graph.CausalGraph 方法)
adjustment_(ylearn._why.Why 属性)
ancestors() (ylearn.causal_model.graph.CausalGraph 方法)
apply() (ylearn.estimator_model.causal_tree.CausalTree 方法)
apply() (ylearn.policy.policy_model.PolicyTree 方法)
B
build_sub_graph() (ylearn.causal_model.graph.CausalGraph 方法)
C
c_components(ylearn.causal_model.graph.CausalGraph 属性)
causal_effect() (ylearn._why.Why 方法)
causal_graph() (ylearn._why.Why 方法)
comp_transormer() (ylearn.estimator_model.approximation_bound.ApproxBound 方法)
comp_transormer() (ylearn.estimator_model.deepiv.DeepIV 方法)
comp_transormer() (ylearn.estimator_model.double_ml.DoubleML 方法)
comp_transormer() (ylearn.estimator_model.doubly_robust.DoublyRobust 方法)
comp_transormer() (ylearn.estimator_model.effect_score.RLoss 方法)
covariate_(ylearn._why.Why 属性)
D
decision_path() (ylearn.estimator_model.causal_tree.CausalTree 方法)
decision_path() (ylearn.policy.policy_model.PolicyTree 方法)
descendents() (ylearn.causal_model.graph.CausalGraph 方法)
E
effect_nji() (ylearn.estimator_model.deepiv.DeepIV 方法)
effect_nji() (ylearn.estimator_model.double_ml.DoubleML 方法)
effect_nji() (ylearn.estimator_model.doubly_robust.DoublyRobust 方法)
effect_nji() (ylearn.estimator_model.effect_score.RLoss 方法)
effect_nji() (ylearn.estimator_model.iv.NP2SLS 方法)
effect_nji() (ylearn.estimator_model.meta_learner.SLearner 方法)
effect_nji() (ylearn.estimator_model.meta_learner.TLearner 方法)
effect_nji() (ylearn.estimator_model.meta_learner.XLearner 方法)
estimate() (ylearn.causal_model.CausalModel 方法)
estimate() (ylearn.estimator_model.approximation_bound.ApproxBound 方法)
estimate() (ylearn.estimator_model.causal_tree.CausalTree 方法)
estimate() (ylearn.estimator_model.deepiv.DeepIV 方法)
estimate() (ylearn.estimator_model.double_ml.DoubleML 方法)
estimate() (ylearn.estimator_model.doubly_robust.DoublyRobust 方法)
estimate() (ylearn.estimator_model.iv.NP2SLS 方法)
estimate() (ylearn.estimator_model.meta_learner.SLearner 方法)
estimate() (ylearn.estimator_model.meta_learner.TLearner 方法)
estimate() (ylearn.estimator_model.meta_learner.XLearner 方法)
estimators_(ylearn._why.Why 属性)
explicit_unob_var_dag(ylearn.causal_model.graph.CausalGraph 属性)
F
feature_importance(ylearn.estimator_model.causal_tree.CausalTree 属性)
feature_importance(ylearn.policy.policy_model.PolicyTree 属性)
feature_names_in_(ylearn._why.Why 属性)
fit() (ylearn._why.Why 方法)
fit() (ylearn.effect_interpreter.ce_interpreter.CEInterpreter 方法)
fit() (ylearn.estimator_model.approximation_bound.ApproxBound 方法)
fit() (ylearn.estimator_model.causal_tree.CausalTree 方法)
fit() (ylearn.estimator_model.deepiv.DeepIV 方法)
fit() (ylearn.estimator_model.double_ml.DoubleML 方法)
fit() (ylearn.estimator_model.doubly_robust.DoublyRobust 方法)
fit() (ylearn.estimator_model.effect_score.RLoss 方法)
fit() (ylearn.estimator_model.iv.NP2SLS 方法)
fit() (ylearn.estimator_model.meta_learner.SLearner 方法)
fit() (ylearn.estimator_model.meta_learner.TLearner 方法)
fit() (ylearn.estimator_model.meta_learner.XLearner 方法)
fit() (ylearn.interpreter.policy_interpreter.PolicyInterpreter 方法)
fit() (ylearn.policy.policy_model.PolicyTree 方法)
G
get_backdoor_path() (ylearn.causal_model.CausalModel 方法)
get_backdoor_set() (ylearn.causal_model.CausalModel 方法)
get_depth() (ylearn.policy.policy_model.PolicyTree 方法)
get_frontdoor_set() (ylearn.causal_model.CausalModel 方法)
get_iv() (ylearn.causal_model.CausalModel 方法)
get_n_leaves() (ylearn.policy.policy_model.PolicyTree 方法)
H
has_collider() (ylearn.causal_model.CausalModel 方法)
I
id() (ylearn.causal_model.CausalModel 方法)
identifier_(ylearn._why.Why 属性)
identify() (ylearn._why.Why 方法)
identify() (ylearn.causal_model.CausalModel 方法)
identify_estimate() (ylearn.causal_model.CausalModel 方法)
individual_causal_effect() (ylearn._why.Why 方法)
instrument_(ylearn._why.Why 属性)
interpret() (ylearn.effect_interpreter.ce_interpreter.CEInterpreter 方法)
interpret() (ylearn.interpreter.policy_interpreter.PolicyInterpreter 方法)
is_connected_backdoor_path() (ylearn.causal_model.CausalModel 方法)
is_frontdoor_set() (ylearn.causal_model.CausalModel 方法)
is_valid_backdoor_set() (ylearn.causal_model.CausalModel 方法)
is_valid_iv() (ylearn.causal_model.CausalModel 方法)
N
n_features_(ylearn.policy.policy_model.PolicyTree 属性)
O
observed_dag(ylearn.causal_model.graph.CausalGraph 属性)
outcome_(ylearn._why.Why 属性)
P
parents() (ylearn.causal_model.graph.CausalGraph 方法)
parse() (ylearn.causal_model.prob.Prob 方法)
plot() (ylearn.effect_interpreter.ce_interpreter.CEInterpreter 方法)
plot() (ylearn.interpreter.policy_interpreter.PolicyInterpreter 方法)
plot() (ylearn.policy.policy_model.PolicyTree 方法)
plot_causal_graph() (ylearn._why.Why 方法)
plot_causal_tree() (ylearn.estimator_model.causal_tree.CausalTree 方法)
plot_policy_interpreter() (ylearn._why.Why 方法)
policy_interpreter() (ylearn._why.Why 方法)
predict_ind() (ylearn.policy.policy_model.PolicyTree 方法)
predict_opt_effect() (ylearn.policy.policy_model.PolicyTree 方法)
preprocessor_(ylearn._why.Why 属性)
R
remove_edge() (ylearn.causal_model.graph.CausalGraph 方法)
remove_edges_from() (ylearn.causal_model.graph.CausalGraph 方法)
remove_incoming_edges() (ylearn.causal_model.graph.CausalGraph 方法)
remove_nodes() (ylearn.causal_model.graph.CausalGraph 方法)
remove_outgoing_edges() (ylearn.causal_model.graph.CausalGraph 方法)
S
score() (ylearn._why.Why 方法)
score() (ylearn.estimator_model.effect_score.RLoss 方法)
show_latex_expression() (ylearn.causal_model.prob.Prob 方法)
T
topo_order(ylearn.causal_model.graph.CausalGraph 属性)
treatment_(ylearn._why.Why 属性)
U
uplift_model() (ylearn._why.Why 方法)
W
whatif() (ylearn._why.Why 方法)
Y
y_encoder_(ylearn._why.Why 属性)
ylearn._why.Why(内置类)
ylearn.causal_model.CausalModel(内置类)
ylearn.causal_model.graph.CausalGraph(内置类)
ylearn.causal_model.prob.Prob(内置类)
ylearn.effect_interpreter.ce_interpreter.CEInterpreter(内置类)
ylearn.estimator_model.approximation_bound.ApproxBound(内置类)
ylearn.estimator_model.causal_tree.CausalTree(内置类)
ylearn.estimator_model.deepiv.DeepIV(内置类)
ylearn.estimator_model.double_ml.DoubleML(内置类)
ylearn.estimator_model.doubly_robust.DoublyRobust(内置类)
ylearn.estimator_model.effect_score.RLoss(内置类)
ylearn.estimator_model.iv.NP2SLS(内置类)
ylearn.estimator_model.meta_learner.SLearner(内置类)
ylearn.estimator_model.meta_learner.TLearner(内置类)
ylearn.estimator_model.meta_learner.XLearner(内置类)
ylearn.interpreter.policy_interpreter.PolicyInterpreter(内置类)
ylearn.policy.policy_model.PolicyTree(内置类)
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