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 651–675 of 1722 papers

TitleStatusHype
Generalized Random Forests using Fixed-Point TreesCode0
Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features—0
FAIR: A Causal Framework for Accurately Inferring Judgments Reversals—0
CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems—0
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal InferenceCode1
Identifiable causal inference with noisy treatment and no side informationCode0
Can predictive models be used for causal inference?—0
Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding—0
Mitigating Cold-start Forecasting using Cold Causal Demand Forecasting Model—0
Inferring dynamic regulatory interaction graphs from time series data with perturbations—0
A Brief Review of Hypernetworks in Deep LearningCode0
Leveraging text data for causal inference using electronic health recordsCode0
Can Large Language Models Infer Causation from Correlation?Code1
Task-specific experimental design for treatment effect estimation—0
A Causal Framework for Decomposing Spurious Variations—0
Timing Process Interventions with Causal Inference and Reinforcement Learning—0
Finding Counterfactually Optimal Action Sequences in Continuous State SpacesCode0
Intervention Generalization: A View from Factor Graph Models—0
Contagion Effect Estimation Using Proximal Embeddings—0
Individual Causal Inference Using Panel Data With Multiple Outcomes—0
Behavioral Causal Inference—0
Point Cloud Completion Guided by Prior Knowledge via Causal Inference—0
Inferring Individual Direct Causal Effects Under Heterogeneous Peer InfluenceCode0
Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers—0
Neuroevolutionary representations for learning heterogeneous treatment effectsCode0
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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