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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

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Showing 151–175 of 1722 papers

TitleStatusHype
Causal Effect Inference with Deep Latent-Variable ModelsCode1
Dynamic Causal Bayesian OptimizationCode1
Empirical Analysis of Model Selection for Heterogeneous Causal Effect EstimationCode1
Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through LogicCode1
Estimating Multi-cause Treatment Effects via Single-cause PerturbationCode1
A Survey on Causal Inference for RecommendationCode1
Adversarial Counterfactual Learning and Evaluation for Recommender SystemCode1
Estimating Transfer Entropy via Copula EntropyCode1
A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric ApproachesCode1
MiranDa: Mimicking the Learning Processes of Human Doctors to Achieve Causal Inference for Medication RecommendationCode1
Generalizing Graph Neural Networks on Out-Of-Distribution GraphsCode1
General targeted machine learning for modern causal mediation analysisCode1
Contextual Debiasing for Visual Recognition With Causal MechanismsCode1
Causal Incremental Graph Convolution for Recommender System RetrainingCode1
CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science ImagesCode1
Causal Image Modeling for Efficient Visual UnderstandingCode1
A Brief Introduction to Causal Inference in Machine LearningCode1
Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention RecognitionCode1
Causal Inference for Qualitative OutcomesCode1
Causal Inference in Recommender Systems: A Survey of Strategies for Bias Mitigation, Explanation, and GeneralizationCode1
Causal Inference for Spatial TreatmentsCode1
Causal Inference in Recommender Systems: A Survey and Future DirectionsCode1
Invariant Causal Prediction for Block MDPsCode1
Discriminative and Consistent Representation DistillationCode1
Learning from Counterfactual Links for Link PredictionCode1
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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