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 601–625 of 1722 papers

TitleStatusHype
Beyond Bell's Theorem II: Scenarios with arbitrary causal structure—0
Ancestral Instrument Method for Causal Inference without Complete Knowledge—0
Causal Machine Learning for Healthcare and Precision Medicine—0
Better Decisions through the Right Causal World Model—0
Causally-guided Regularization of Graph Attention Improves Generalizability—0
Causally Fair Node Classification on Non-IID Graph Data—0
-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap—0
Causally Consistent Normalizing Flow—0
Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction—0
Causal Links Between Anthropogenic Emissions and Air Pollution Dynamics in Delhi—0
Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression—0
An Automated Approach to Causal Inference in Discrete Settings—0
Adaptive Multi-Source Causal Inference—0
A causal inference approach of monosynapses from spike trains—0
Transfer Learning for Individual Treatment Effect Estimation—0
Benchmarking Causal Study to Interpret Large Language Models for Source Code—0
Causal knowledge graph analysis identifies adverse drug effects—0
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation—0
Analyzing Behaviors of Mixed Traffic via Reinforcement Learning at Unsignalized Intersections—0
Causality in the Can: Diet Coke's Impact on Fatness—0
Causality in cognitive neuroscience: concepts, challenges, and distributional robustness—0
Behavioral Causal Inference—0
Causality in cardiorespiratory signals in pediatric cardiac patients—0
Be Causal: De-biasing Social Network Confounding in Recommendation—0
Analysis of cause-effect inference by comparing regression errors—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