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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 951–975 of 1722 papers

TitleStatusHype
Towards Causal Representation Learning—0
Towards Clarifying the Theory of the Deconfounder—0
Towards Context-Aware Emotion Recognition Debiasing from a Causal Demystification Perspective via De-confounded Training—0
Towards Deconfounded Image-Text Matching with Causal Inference—0
Higher order definition of causality by optimally conditioned transfer entropy—0
Towards Generalizing Inferences from Trials to Target Populations—0
Towards Measuring Sell Side Outcomes in Buy Side Marketplace Experiments using In-Experiment Bipartite Graph—0
Towards Modeling the Interaction of Spatial-Associative Neural Network Representations for Multisensory Perception—0
Towards Principled Causal Effect Estimation by Deep Identifiable Models—0
Transcriptional Response of SK-N-AS Cells to Methamidophos—0
Transfer Learning for Estimating Causal Effects using Neural Networks—0
Interacting Treatments with Endogenous Takeup—0
Revealing Treatment Non-Adherence Bias in Clinical Machine Learning Using Large Language Models—0
TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models—0
TSCI: two stage curvature identification for causal inference with invalid instruments—0
Tutorial: Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System—0
TV-SurvCaus: Dynamic Representation Balancing for Causal Survival Analysis—0
Position: AI/ML Influencers Have a Place in the Academic Process—0
Unbiased Scene Graph Generation via Two-stage Causal Modeling—0
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference—0
Unconditional Randomization Tests for Interference—0
Graph Neural Networks for Causal Inference Under Network Confounding—0
Uncovering Bias Mechanisms in Observational Studies—0
Understanding Perceptual and Conceptual Fluency at a Large Scale—0
Understanding the Humans Behind Online Misinformation: An Observational Study Through the Lens of the COVID-19 Pandemic—0
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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