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 15011525 of 1722 papers

TitleStatusHype
CausalRec: Causal Inference for Visual Debiasing in Visually-Aware RecommendationCode0
Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary PerspectiveCode0
How Fragile is Relation Extraction under Entity Replacements?Code0
Entropic Causal InferenceCode0
Can Large Language Models (or Humans) Disentangle Text?Code0
Outcome-adaptive lasso: variable selection for causal inferenceCode0
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural CircuitsCode0
Towards a Learning Theory of Cause-Effect InferenceCode0
Learning Representations for Counterfactual InferenceCode0
Environment Invariant Linear Least SquaresCode0
Learning Representations of Instruments for Partial Identification of Treatment EffectsCode0
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging DatasetsCode0
A Survey on Causal Representation Learning and Future Work for Medical Image AnalysisCode0
Learning sources of variability from high-dimensional observational studiesCode0
Sample Constrained Treatment Effect EstimationCode0
Estimating Buildings' Parameters over Time Including Prior KnowledgeCode0
Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured NoiseCode0
Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal ProxiesCode0
A Survey on Causal InferenceCode0
Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injuryCode0
Learning to search efficiently for causally near-optimal treatmentsCode0
Learning Treatment Effects in Panels with General Intervention PatternsCode0
Estimating Causal Effects with the Neural Autoregressive Density EstimatorCode0
ParKCa: Causal Inference with Partially Known CausesCode0
Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement LearningCode0
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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