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 951–975 of 1722 papers

TitleStatusHype
Reimagining Urban Science: Scaling Causal Inference with Large Language Models—0
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions—0
Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace—0
Reinterpreting causal discovery as the task of predicting unobserved joint statistics—0
Reinterpreting demand estimation—0
Relating Graph Neural Networks to Structural Causal Models—0
A Framework of decision-relevant observability: Reinforcement Learning converges under relative ignorability—0
Reliable Beamforming at Terahertz Bands: Are Causal Representations the Way Forward?—0
Reliable Estimation of Individual Treatment Effect with Causal Information Bottleneck—0
ReLU integral probability metric and its applications—0
Removing Hidden Confounding by Experimental Grounding—0
Representation Disentaglement via Regularization by Causal Identification—0
Representation learning with CGAN for casual inference—0
Research on Personal Credit Risk Assessment Methods Based on Causal Inference—0
Restricted Causal Inference Algorithm—0
Rethinking Distributional IVs: KAN-Powered D-IV-LATE & Model Choice—0
Retrospective Causal Inference with Machine Learning Ensembles: An Application to Anti-Recidivism Policies in Colombia—0
A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks—0
Revisiting the General Identifiability Problem—0
Revolutionizing Clinical Trials: A Manifesto for AI-Driven Transformation—0
Riemannian Metric Learning: Closer to You than You Imagine—0
Robust agents learn causal world models—0
Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items—0
Robust Identifiability in Linear Structural Equation Models of Causal Inference—0
ROCK: A Causal Inference Framework for Reasoning about Commonsense Causality—0
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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