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 1651–1675 of 1722 papers

TitleStatusHype
Do LLMs Act as Repositories of Causal Knowledge?—0
Domain Adaptable Prescriptive AI Agent for Enterprise—0
Don't (fully) exclude me, it's not necessary! Causal inference with semi-IVs—0
Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data—0
Double Machine Learning for Conditional Moment Restrictions: IV Regression, Proximal Causal Learning and Beyond—0
DoubleMLDeep: Estimation of Causal Effects with Multimodal Data—0
Online Multi-Armed Bandits with Adaptive Inference—0
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks—0
DROID: Driver-centric Risk Object Identification—0
DRTCI: Learning Disentangled Representations for Temporal Causal Inference—0
Dynamical causality under invisible confounders—0
Dynamic Regularized CBDT: Variance-Calibrated Causal Boosting for Interpretable Heterogeneous Treatment Effects—0
DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models—0
Dynamic Survival Transformers for Causal Inference with Electronic Health Records—0
Early Identification of Pathogenic Social Media Accounts—0
Eco-efficiency as a Catalyst for Citizen Co-production: Evidence from Chinese Cities—0
Economic Causal Inference Based on DML Framework: Python Implementation of Binary and Continuous Treatment Variables—0
Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments—0
Effect of secular trend in drug effectiveness study in real world data—0
Efficient Computation of Counterfactual Bounds—0
Efficient Counterfactual Learning from Bandit Feedback—0
Causal Inference of General Treatment Effects using Neural Networks with A Diverging Number of Confounders—0
Efficient estimation of weighted cumulative treatment effects by double/debiased machine learning—0
EI-CLIP: Entity-Aware Interventional Contrastive Learning for E-Commerce Cross-Modal Retrieval—0
Embodied Scene-aware Human Pose Estimation—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