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

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Showing 651–675 of 1722 papers

TitleStatusHype
A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis—0
Directed Acyclic Graph Convolutional Networks—0
A primer on optimal transport for causal inference with observational data—0
Causal Analysis of ASR Errors for Children: Quantifying the Impact of Physiological, Cognitive, and Extrinsic Factors—0
A Novel Method to Metigate Demographic and Expert Bias in ICD Coding with Causal Inference—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
Compositional Models for Estimating Causal Effects—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
Complementary Advantages of ChatGPTs and Human Readers in Reasoning: Evidence from English Text Reading Comprehension—0
Debiasing Alternative Data for Credit Underwriting Using Causal Inference—0
Debiasing Conditional Stochastic Optimization—0
Causal Estimation with Functional Confounders—0
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges—0
Differentially Private Multi-Site Treatment Effect Estimation—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
Causal Analysis and Classification of Traffic Crash Injury Severity Using Machine Learning Algorithms—0
Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions—0
A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery—0
Accurate Use of Label Dependency in Multi-Label Text Classification Through the Lens of Causality—0
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference—0
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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