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 1301–1325 of 1722 papers

TitleStatusHype
Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection—0
A Way to Synthetic Triple Difference—0
Axiomatization of Interventional Probability Distributions—0
Balanced Linear Contextual Bandits—0
Batch-Adaptive Annotations for Causal Inference with Complex-Embedded Outcomes—0
Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment—0
A Bayesian Model for Bivariate Causal Inference—0
Bayesian Causal Inference in Doubly Gaussian DAG-probit Models—0
Bayesian causal inference via probabilistic program synthesis—0
Bayesian Counterfactual Prediction Models for HIV Care Retention with Incomplete Outcome and Covariate Information—0
Bayesian Discovery of Linear Acyclic Causal Models—0
Bayesian Evolutionary Swarm Architecture: A Formal Epistemic System Grounded in Truth-Based Competition—0
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms—0
Identification and Inference for Synthetic Control Methods with Spillover Effects: Estimating the Economic Cost of the Sudan Split—0
BayesIMP: Uncertainty Quantification for Causal Data Fusion—0
Be Causal: De-biasing Social Network Confounding in Recommendation—0
Behavioral Causal Inference—0
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation—0
Benchmarking Causal Study to Interpret Large Language Models for Source Code—0
Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression—0
-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap—0
Better Decisions through the Right Causal World Model—0
Beyond Bell's Theorem II: Scenarios with arbitrary causal structure—0
Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine—0
Beyond Flatland: A Geometric Take on Matching Methods for Treatment Effect Estimation—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