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 1376–1400 of 1722 papers

TitleStatusHype
Causal Discovery on the Effect of Antipsychotic Drugs on Delirium Patients in the ICU using Large EHR Dataset—0
The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study—0
Causal Discovery with Stage Variables for Health Time Series—0
Causal Disentanglement for Regulating Social Influence Bias in Social Recommendation—0
Causal Disentanglement with Network Information for Debiased Recommendations—0
Causal Effect Estimation: Recent Advances, Challenges, and Opportunities—0
Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion—0
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach—0
Causal Effect Variational Autoencoder with Uniform Treatment—0
Causal Estimation with Functional Confounders—0
Causal-Ex: Causal Graph-based Micro and Macro Expression Spotting—0
Causal Feature Selection with Dimension Reduction for Interpretable Text Classification—0
Causal Post-Processing of Predictive Models—0
Causal Forecasting for Pricing—0
Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks—0
Causal Graph Aided Causal Discovery in an Observational Aneurysmal Subarachnoid Hemorrhage Study—0
Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models—0
Causal Identification with Additive Noise Models: Quantifying the Effect of Noise—0
Identifying causal effects with subjective ordinal outcomes—0
Causal Inference and Data Fusion in Econometrics—0
Causal inference approach to appraise long-term effects of maintenance policy on functional performance of asphalt pavements—0
Causal Inference Based Single-branch Ensemble Trees For Uplift Modeling—0
Causal Inference based Transfer Learning with LLMs: An Efficient Framework for Industrial RUL Prediction—0
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components—0
Causal Inference by Stochastic Complexity—0
Show:102550
← PrevPage 56 of 69Next →
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