mlx_causal (mlx v0.2.0)

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Summary

Functions

Functions

attribution_analysis(Data, Treatment, Options)

backdoor_adjustment(Graph, Treatment, Outcome)

causal_benchmark_suite(Datasets, Methods)

causal_discovery_metrics(Predicted, True, Options)

causal_discovery_with_latents(Data, Options)

causal_effect_estimation(Data, Treatment, Outcome, Confounders)

causal_explanation(Model, Instance, Options)

causal_fairness_analysis(Data, Protected, Treatment, Outcome)

causal_forests(Data, Treatment, Outcome, Features)

causal_gan(Data, Treatment, Generator, Discriminator)

causal_graph_neural_networks(GraphData, NodeFeatures, CausalAdjacency, TargetNodes)

causal_meta_learning(Tasks, Models, Adaptation, Options)

causal_model_validation(Model, TestData, GroundTruthGraph)

causal_policy_learning(States, Actions, Rewards, CausalGraph)

causal_representation_learning(Data, Architecture, Options)

causal_time_series_forecasting(TimeSeries, Treatment, Horizon, Options)

causal_transformer(Data, Architecture, Options)

causal_vae(Data, TreatmentVar, LatentDim)

closest_world_counterfactuals(Model, FactualWorld, CounterfactualQuery)

confounded_bandits(Arms, Confounders, Rewards, Policy)

constraint_based_discovery(Data, Constraints, Options)

contrastive_explanation(Model, Factual, Counterfactual, Options)

counterfactual_generator(Model, Evidence, Options)

counterfactual_inference(StructuralModel, Intervention, Evidence, Query)

deep_structural_model(Data, Structure, Architecture, Options)

difference_in_differences(PreTreatment, PostTreatment, Control, Options)

do_calculus(Graph, Query, Evidence)

domain_adaptation_causal(SourceData, TargetData, Model, Options)

double_ml(Treatment, Outcome, Confounders, MLModels)

dynamic_causal_modeling(TimeSeries, Structure, Parameters, Options)

fast_causal_inference(Data, Options)

federated_causal_learning(Clients, Data, Models, Options)

frontdoor_adjustment(Graph, Treatment, Outcome)

ges_algorithm(Data, Penalties)

granger_causality(TimeSeries1, TimeSeries2, Options)

hybrid_causal_discovery(Data, Constraints, Options)

instrumental_variables(Treatment, Outcome, Instrument)

intervention_simulation(Model, Interventions, Targets, Options)

invariant_risk_minimization(Domains, Features, Labels)

mediation_analysis(Data, Treatment, Mediator, Outcome)

moderation_analysis(Data, Treatment, Moderator, Outcome)

neural_causal_model(Features, Treatment, Outcome, Architecture)

offline_causal_rl(States, Actions, Rewards, Policy)

pc_algorithm(Data, Alpha)

probabilistic_causal_programming(Program, Evidence, Query)

probabilistic_counterfactuals(Model, Evidence, Query)

quantum_causal_models(QuantumStates, CausalStructure, Measurements)

regime_switching_causal(Data, Regimes, Transitions, Options)

regression_discontinuity(Data, Cutoff, Options)

robust_causal_inference(Data, Models, Robustness, Options)

score_based_discovery(Data, Score, Options)

sensitivity_analysis(CausalModel, Parameters, Perturbations, Metrics)

structural_counterfactuals(Model, Evidence, Intervention, Query)

synthetic_control(Treatment, Control, Outcome, Options)

targeted_ml(Data, Treatment, Outcome, Models)

temporal_causal_discovery(TimeSeries, Lags, Options)

treatment_effect_validation(Model, Data, TrueEffects, Options)

var_causal_analysis(TimeSeries, Order, Options)