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 1226–1250 of 1722 papers

TitleStatusHype
Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items—0
Experimentally testable whole brain manifolds that recapitulate behavior—0
Machine learning in the social and health sciences—0
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
Error Causal inference for Multi-Fusion models—0
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests—0
Adaptive Multi-Source Causal Inference—0
Stochastic Intervention for Causal Inference via Reinforcement Learning—0
Stochastic Intervention for Causal Effect Estimation—0
Variational Causal Autoencoder for Interventional and Counterfactual Queries—0
Counterfactual Invariance to Spurious Correlations in Text Classification—0
Identifying and Estimating Causal Effects under Weak Overlap by Generative Prognostic Model—0
Be Causal: De-biasing Social Network Confounding in Recommendation—0
Choice Set Confounding in Discrete ChoiceCode0
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
Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding—0
Weak Instrumental Variables: Limitations of Traditional 2SLS and Exploring Alternative Instrumental Variable Estimators—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