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 676–700 of 1722 papers

TitleStatusHype
A Novel Method to Metigate Demographic and Expert Bias in ICD Coding with Causal Inference—0
Compositional Models for Estimating Causal Effects—0
Deep End-to-end Causal Inference—0
Causal Post-Processing of Predictive Models—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
Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks—0
Deep Learning With DAGs—0
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training—0
Causal Graph Aided Causal Discovery in an Observational Aneurysmal Subarachnoid Hemorrhage Study—0
Complementary Advantages of ChatGPTs and Human Readers in Reasoning: Evidence from English Text Reading Comprehension—0
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges—0
Density-based interpretable hypercube region partitioning for mixed numeric and categorical data—0
Deoxys: A Causal Inference Engine for Unhealthy Node Mitigation in Large-scale Cloud Infrastructure—0
Causal Identification with Additive Noise Models: Quantifying the Effect of Noise—0
Optimal Nonparametric Inference with Two-Scale Distributional Nearest Neighbors—0
Designing Algorithmic Recommendations to Achieve Human-AI Complementarity—0
Identifying causal effects with subjective ordinal outcomes—0
Causal Analysis and Classification of Traffic Crash Injury Severity Using Machine Learning Algorithms—0
Nested Nonparametric Instrumental Variable Regression—0
Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions—0
Developing a general-purpose clinical language inference model from a large corpus of clinical notes—0
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference—0
Comment on "Blessings of Multiple Causes"—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