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 1151–1175 of 1722 papers

TitleStatusHype
ZaliQL: A SQL-Based Framework for Drawing Causal Inference from Big Data—0
Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations—0
Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry—0
A Multi-class Ride-hailing Service Subsidy System Utilizing Deep Causal Networks—0
Long-term Causal Inference via Modeling Sequential Latent Confounding—0
Learning Exposure Mapping Functions for Inferring Heterogeneous Peer Effects—0
A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference—0
A Fast Kernel-based Conditional Independence test with Application to Causal Discovery—0
A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation—0
Correlation vs causation in Alzheimer's disease: an interpretability-driven study—0
Causal Fairness Assessment of Treatment Allocation with Electronic Health Records—0
A Bayesian Semiparametric Method For Estimating Causal Quantile Effects—0
A Bayesian Solution to the M-Bias Problem—0
A category theoretical argument for causal inference—0
A Causal Adjustment Module for Debiasing Scene Graph Generation—0
A Causal Approach for Business Optimization: Application on an Online Marketplace—0
A Causal Framework for Decomposing Spurious Variations—0
A Causal Framework for Precision Rehabilitation—0
A Causal Inference Approach for Quantifying Research Impact—0
A causal inference approach of monosynapses from spike trains—0
A Causal Inference Framework for Data Rich Environments—0
A Causal Inference Framework for Leveraging External Controls in Hybrid Trials—0
A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization—0
A Causal Lens for Controllable Text Generation—0
A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Model—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