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 1551–1575 of 1722 papers

TitleStatusHype
Evaluating Fairness Metrics in the Presence of Dataset Bias—0
Causal Inference and Mechanism Clustering of A Mixture of Additive Noise ModelsCode0
Efficient Counterfactual Learning from Bandit Feedback—0
A Primer on Causality in Data Science—0
Learning Optimal Fair Policies—0
Understanding Perceptual and Conceptual Fluency at a Large Scale—0
Optimal Nonparametric Inference with Two-Scale Distributional Nearest Neighbors—0
Transfer Learning for Estimating Causal Effects using Neural Networks—0
Discovering Context Specific Causal Relationships—0
The Deconfounded Recommender: A Causal Inference Approach to Recommendation—0
Effect of secular trend in drug effectiveness study in real world data—0
Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms—0
Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity DesignsCode0
Local Linear ForestsCode0
Applications of Common Entropy for Causal Inference—0
Towards Modeling the Interaction of Spatial-Associative Neural Network Representations for Multisensory Perception—0
Cause-Effect Deep Information Bottleneck For Systematically Missing Covariates—0
Causal Inference for Early Detection of Pathogenic Social Media Accounts—0
Surrogate Outcomes and TransportabilityCode0
Simplifying Probabilistic Expressions in Causal Inference—0
Interpretable Almost Matching Exactly for Causal InferenceCode0
Orthogonal Random Forest for Causal Inference—0
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary NoiseCode0
Causal Inference with Noisy and Missing Covariates via Matrix FactorizationCode0
Too Fast Causal Inference under Causal Insufficiency—0
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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