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 951975 of 1722 papers

TitleStatusHype
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by TreatmentCode0
Causal Inference from Small High-dimensional Datasets0
Framework for inferring empirical causal graphs from binary data to support multidimensional poverty analysisCode0
SoK: Blockchain DecentralizationCode1
DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulationCode1
The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventionsCode0
XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression ExtractionCode0
CaM-Gen: Causally Aware Metric-Guided Text Generation0
Conceptualizing Treatment Leakage in Text-based Causal Inference0
Causal Discovery on the Effect of Antipsychotic Drugs on Delirium Patients in the ICU using Large EHR Dataset0
Partial Identification of Dose Responses with Hidden Confounders0
Local Gaussian process extrapolation for BART models with applications to causal inference0
An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random ForestCode0
Adversarial Estimators0
A unified theory of information transfer and causal relation0
Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction0
Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition0
Causal Disentanglement with Network Information for Debiased Recommendations0
Statistical Perspective on Functional and Causal Neural Connectomics: The Time-Aware PC AlgorithmCode1
Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge0
Machine Learning and Deep Learning -- A review for EcologistsCode0
Causality, Causal Discovery, and Causal Inference in Structural Engineering0
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender SystemCode1
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging DatasetsCode0
SurvCaus : Representation Balancing for Survival Causal InferenceCode0
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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