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 1176–1200 of 1722 papers

TitleStatusHype
Variational Causal Networks: Approximate Bayesian Inference over Causal StructuresCode1
Harmonization with Flow-based Causal InferenceCode0
Conterfactual Generative Zero-Shot Semantic Segmentation—0
Context-Specific Causal Discovery for Categorical Data Using Staged Trees—0
BayesIMP: Uncertainty Quantification for Causal Data Fusion—0
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data—0
Learning Treatment Effects in Panels with General Intervention PatternsCode0
Learning from Counterfactual Links for Link PredictionCode1
Error Causal inference for Multi-Fusion models—0
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests—0
Federated Estimation of Causal Effects from Observational DataCode1
Adaptive Multi-Source Causal Inference—0
Stochastic Intervention for Causal Inference via Reinforcement Learning—0
Stochastic Intervention for Causal Effect Estimation—0
Counterfactual Invariance to Spurious Correlations in Text Classification—0
Identifying and Estimating Causal Effects under Weak Overlap by Generative Prognostic Model—0
Variational Causal Autoencoder for Interventional and Counterfactual Queries—0
Choice Set Confounding in Discrete ChoiceCode0
Be Causal: De-biasing Social Network Confounding in Recommendation—0
Estimating Heterogeneous Causal Effect of Polysubstance Usage on Drug Overdose from Large-Scale Electronic Health Record—0
Causally motivated Shortcut Removal Using Auxiliary LabelsCode0
Causal Inference in medicine and in health policy, a summary—0
The Local Approach to Causal Inference under Network Interference—0
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding—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