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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

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Showing 1–10 of 1722 papers

TitleStatusHype
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning—0
Causal-Aware Intelligent QoE Optimization for VR Interaction with Adaptive Keyframe Extraction—0
Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies—0
Bayesian Evolutionary Swarm Architecture: A Formal Epistemic System Grounded in Truth-Based Competition—0
T-CPDL: A Temporal Causal Probabilistic Description Logic for Developing Logic-RAG Agent—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
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Benchmark Results

#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