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 626–650 of 1722 papers

TitleStatusHype
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests—0
Computational identification of ketone metabolism as a key regulator of sleep stability and circadian dynamics via real-time metabolic profiling—0
Counterfactual Invariance to Spurious Correlations in Text Classification—0
Counterfactually Fair Regression with Double Machine Learning—0
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing—0
Computational Causal Inference—0
Causal and anti-causal learning in pattern recognition for neuroimaging—0
Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation—0
Counterfactual Reasoning and Learning Systems—0
Estimating a Directed Tree for Extremes—0
A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis—0
Counterfactual Representation Learning with Balancing Weights—0
Counterfactual Thinking for Long-tailed Information Extraction—0
Deep End-to-end Causal Inference—0
Causal Analysis of ASR Errors for Children: Quantifying the Impact of Physiological, Cognitive, and Extrinsic Factors—0
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees—0
Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimation—0
A Novel Method to Metigate Demographic and Expert Bias in ICD Coding with Causal Inference—0
Credit Ratings: Heterogeneous Effect on Capital Structure—0
CRepair: CVAE-based Automatic Vulnerability Repair Technology—0
Cross-Dataset Propensity Estimation for Debiasing Recommender Systems—0
Compositional Models for Estimating Causal Effects—0
Crowd Sensing and Living Lab Outdoor Experimentation Made Easy—0
CRTRE: Causal Rule Generation with Target Trial Emulation Framework—0
Complementary Advantages of ChatGPTs and Human Readers in Reasoning: Evidence from English Text Reading Comprehension—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