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

Showing 126150 of 1722 papers

TitleStatusHype
A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift ModelingCode1
A framework for causal segmentation analysis with machine learning in large-scale digital experimentsCode1
VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual QueriesCode1
Dynamic Causal Bayesian OptimizationCode1
Counterfactual Debiasing Inference for Compositional Action RecognitionCode1
Optimization-based Causal Estimation from Heterogenous EnvironmentsCode1
Uncovering Main Causalities for Long-tailed Information ExtractionCode1
Counterfactual Explainable RecommendationCode1
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identificationCode1
Causal Incremental Graph Convolution for Recommender System RetrainingCode1
Towards Unbiased Visual Emotion Recognition via Causal InterventionCode1
Auto IV: Counterfactual Prediction via Automatic Instrumental Variable DecompositionCode1
Algorithmic Causal Effect Identification with causaleffectCode1
Spatiotemporal information conversion machine for time-series predictionCode1
The Causal-Neural Connection: Expressiveness, Learnability, and InferenceCode1
Causal Reinforcement Learning using Observational and Interventional DataCode1
CausalNLP: A Practical Toolkit for Causal Inference with TextCode1
Variational Causal Networks: Approximate Bayesian Inference over Causal StructuresCode1
Learning from Counterfactual Links for Link PredictionCode1
Federated Estimation of Causal Effects from Observational DataCode1
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
Everything Has a Cause: Leveraging Causal Inference in Legal Text AnalysisCode1
SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic EventsCode1
SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation ModelsCode1
A Structural Causal Model for MR Images of Multiple SclerosisCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAverage Treatment Effect Error0.96Unverified
2Balancing Linear RegressionAverage Treatment Effect Error0.93Unverified
3k-NNAverage Treatment Effect Error0.79Unverified
4CEVAEAverage Treatment Effect Error0.46Unverified
5Balancing Neural NetworkAverage Treatment Effect Error0.42Unverified
6Causal ForestAverage Treatment Effect Error0.4Unverified
7BCAUS DRAverage Treatment Effect Error0.29Unverified
8TARNetAverage Treatment Effect Error0.28Unverified
9Counterfactual Regression + WASSAverage Treatment Effect Error0.27Unverified
10MTDL-KNNAverage Treatment Effect Error0.23Unverified
#ModelMetricClaimedVerifiedStatus
1CFR WASSAverage Treatment Effect on the Treated Error0.09Unverified
2CFR MMDAverage Treatment Effect on the Treated Error0.08Unverified
3BARTAverage Treatment Effect on the Treated Error0.08Unverified
4GANITEAverage Treatment Effect on the Treated Error0.06Unverified
5BCAUSSAverage Treatment Effect on the Treated Error0.05Unverified
#ModelMetricClaimedVerifiedStatus
1BARTAverage Treatment Effect Error0.34Unverified
2OLS with separate regressors for each treatmentAverage Treatment Effect Error0.31Unverified
3Average Treatment Effect Error-0.23Unverified