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 476–500 of 1722 papers

TitleStatusHype
Causal knowledge graph analysis identifies adverse drug effects—0
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach—0
Transfer Learning for Individual Treatment Effect Estimation—0
Causal Links Between Anthropogenic Emissions and Air Pollution Dynamics in Delhi—0
Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction—0
Causally Consistent Normalizing Flow—0
Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression—0
Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion—0
Causally-guided Regularization of Graph Attention Improves Generalizability—0
Better Decisions through the Right Causal World Model—0
Causal Machine Learning for Healthcare and Precision Medicine—0
Causal Mediation Analysis Leveraging Multiple Types of Summary Statistics Data—0
Causal Mediation Analysis with Hidden Confounders—0
A rational model of causal inference with continuous causes—0
Adversarial Orthogonal Regression: Two non-Linear Regressions for Causal Inference—0
Causal Model Analysis using Collider v-structure with Negative Percentage Mapping—0
A primer on optimal transport for causal inference with observational data—0
Causal Effect Estimation: Recent Advances, Challenges, and Opportunities—0
Causal modelling without introducing counterfactuals or abstract distributions—0
Causes of evolutionary divergence in prostate cancer—0
Causal models in string diagrams—0
Causal Multi-Level Fairness—0
Causal nearest neighbor rules for optimal treatment regimes—0
A Primer on Causality in Data Science—0
Causal Disentanglement with Network Information for Debiased Recommendations—0
Show:102550
← PrevPage 20 of 69Next →
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