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 2650 of 1722 papers

TitleStatusHype
Training a Resilient Q-Network against Observational InterferenceCode1
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
Causal Inference for Spatial TreatmentsCode1
Causal Inference for Chatting HandoffCode1
Causal Knowledge Guided Societal Event ForecastingCode1
Causal Inference in Recommender Systems: A Survey and Future DirectionsCode1
Causal Inference with the Instrumental Variable Approach and Bayesian Nonparametric Machine LearningCode1
Causal Modeling of Twitter Activity During COVID-19Code1
causalgraph: A Python Package for Modeling, Persisting and Visualizing Causal Graphs Embedded in Knowledge GraphsCode1
Causal Effect Inference with Deep Latent-Variable ModelsCode1
Causal Image Modeling for Efficient Visual UnderstandingCode1
CausalCite: A Causal Formulation of Paper CitationsCode1
A Graph Neural Network Framework for Causal Inference in Brain NetworksCode1
MiranDa: Mimicking the Learning Processes of Human Doctors to Achieve Causal Inference for Medication RecommendationCode1
Algorithmic Causal Effect Identification with causaleffectCode1
A Brief Introduction to Causal Inference in Machine LearningCode1
A framework for causal segmentation analysis with machine learning in large-scale digital experimentsCode1
Causal Counterfactuals for Improving the Robustness of Reinforcement LearningCode1
CausalEGM: a general causal inference framework by encoding generative modelingCode1
A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift ModelingCode1
CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science ImagesCode1
A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection BiasCode1
Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention RecognitionCode1
Causal Inference for Qualitative OutcomesCode1
Can Large Language Models Infer Causation from Correlation?Code1
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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