SOTAVerified

Causal Inference

Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.

( Image credit: Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data )

Papers

Showing 426450 of 1722 papers

TitleStatusHype
Learning high-dimensional causal effectCode0
Automatic doubly robust inference for linear functionals via calibrated debiased machine learningCode0
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based TrainingCode0
Counterfactual FairnessCode0
Learning Representations of Instruments for Partial Identification of Treatment EffectsCode0
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference ModelsCode0
Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement LearningCode0
Lifted Causal Inference in Relational DomainsCode0
Causal Cartographer: From Mapping to Reasoning Over Counterfactual WorldsCode0
A Causal Framework for Evaluating Deferring SystemsCode0
Causal Inference with CocyclesCode0
Causal Campbell-Goodhart's law and Reinforcement LearningCode0
Logic and Commonsense-Guided Temporal Knowledge Graph CompletionCode0
CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language ModelsCode0
Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's ContinuumCode0
BART: Bayesian additive regression treesCode0
Convolutional neural networks for valid and efficient causal inferenceCode0
Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component AnalysisCode0
Confounding Feature Acquisition for Causal Effect EstimationCode0
Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match QualityCode0
Neural Network Parameter-optimization of Gaussian pmDAGsCode0
Consistent Estimation of Propensity Score Functions with Oversampled Exposed SubjectsCode0
Conditional Cross-Design Synthesis Estimators for Generalizability in MedicaidCode0
An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal InferenceCode0
Conditional Generative Models are Sufficient to Sample from Any Causal Effect EstimandCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAverage Treatment Effect Error0.96Unverified
2Balancing Linear RegressionAverage Treatment Effect Error0.93Unverified
3k-NNAverage Treatment Effect Error0.79Unverified
4CEVAEAverage Treatment Effect Error0.46Unverified
5Balancing Neural NetworkAverage Treatment Effect Error0.42Unverified
6Causal ForestAverage Treatment Effect Error0.4Unverified
7BCAUS DRAverage Treatment Effect Error0.29Unverified
8TARNetAverage Treatment Effect Error0.28Unverified
9Counterfactual Regression + WASSAverage Treatment Effect Error0.27Unverified
10MTDL-KNNAverage Treatment Effect Error0.23Unverified
#ModelMetricClaimedVerifiedStatus
1CFR WASSAverage Treatment Effect on the Treated Error0.09Unverified
2CFR MMDAverage Treatment Effect on the Treated Error0.08Unverified
3BARTAverage Treatment Effect on the Treated Error0.08Unverified
4GANITEAverage Treatment Effect on the Treated Error0.06Unverified
5BCAUSSAverage Treatment Effect on the Treated Error0.05Unverified
#ModelMetricClaimedVerifiedStatus
1BARTAverage Treatment Effect Error0.34Unverified
2OLS with separate regressors for each treatmentAverage Treatment Effect Error0.31Unverified
3Average Treatment Effect Error-0.23Unverified