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 1–25 of 1722 papers

TitleStatusHype
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning—0
Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies—0
Causal-Aware Intelligent QoE Optimization for VR Interaction with Adaptive Keyframe Extraction—0
T-CPDL: A Temporal Causal Probabilistic Description Logic for Developing Logic-RAG Agent—0
Bayesian Evolutionary Swarm Architecture: A Formal Epistemic System Grounded in Truth-Based Competition—0
Causal Interventions in Bond Multi-Dealer-to-Client Platforms—0
An Empirical Comparison of Weak-IV-Robust Procedures in Just-Identified Models—0
International Trade and Intellectual Property—0
From Prompts to Constructs: A Dual-Validity Framework for LLM Research in Psychology—0
Learning Causally Predictable Outcomes from Psychiatric Longitudinal DataCode0
Linear-Time Primitives for Algorithm Development in Graphical Causal Inference—0
Double Machine Learning for Conditional Moment Restrictions: IV Regression, Proximal Causal Learning and Beyond—0
Honesty in Causal Forests: When It Helps and When It Hurts—0
Estimation of Treatment Effects in Extreme and Unobserved Data—0
Rethinking Distributional IVs: KAN-Powered D-IV-LATE & Model Choice—0
Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises—0
Directed Acyclic Graph Convolutional Networks—0
Foundation Models for Causal Inference via Prior-Data Fitted Networks—0
From Images to Insights: Explainable Biodiversity Monitoring with Plain Language Habitat ExplanationsCode0
Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution ShiftsCode1
Correlation vs causation in Alzheimer's disease: an interpretability-driven study—0
STOAT: Spatial-Temporal Probabilistic Causal Inference Network—0
Revolutionizing Clinical Trials: A Manifesto for AI-Driven Transformation—0
Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal DiscoveryCode0
Half-AVAE: Adversarial-Enhanced Factorized and Structured Encoder-Free VAE for Underdetermined Independent Component Analysis—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