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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1601–1625 of 1722 papers

TitleStatusHype
CRepair: CVAE-based Automatic Vulnerability Repair Technology—0
Cross-Dataset Propensity Estimation for Debiasing Recommender Systems—0
Crowd Sensing and Living Lab Outdoor Experimentation Made Easy—0
CRTRE: Causal Rule Generation with Target Trial Emulation Framework—0
CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect—0
Customer Price Sensitivities in Competitive Automobile Insurance Markets—0
Data AUDIT: Identifying Attribute Utility- and Detectability-Induced Bias in Task Models—0
Data Fusion for Partial Identification of Causal Effects—0
Data science is science's second chance to get causal inference right: A classification of data science tasks—0
dcFCI: Robust Causal Discovery Under Latent Confounding, Unfaithfulness, and Mixed Data—0
Debiased Ill-Posed Regression—0
Inference on Strongly Identified Functionals of Weakly Identified Functions—0
Debiased Recommendation with User Feature Balancing—0
Debiasing Alternative Data for Credit Underwriting Using Causal Inference—0
Debiasing Conditional Stochastic Optimization—0
Decoding the Flow: CauseMotion for Emotional Causality Analysis in Long-form Conversations—0
Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features—0
De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts—0
Deconfounded Image Captioning: A Causal Retrospect—0
De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network—0
Deep Causal Learning: Representation, Discovery and Inference—0
Deep End-to-end Causal Inference—0
Deep Learning for Causal Inference—0
Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation—0
Deep Learning Methods for the Noniterative Conditional Expectation G-Formula for Causal Inference from Complex Observational Data—0
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All datasetsIHDPJobsIDHP

Benchmark Results

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