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

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Showing 1651–1700 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
Emoji Promotes Developer Participation and Issue Resolution on GitHub—0
Emoticons vs. Emojis on Twitter: A Causal Inference Approach—0
Data-Driven Influence Functions for Optimization-Based Causal Inference—0
Empowering Vision Transformers with Multi-Scale Causal Intervention for Long-Tailed Image Classification—0
Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs—0
End-to-End Balancing for Causal Continuous Treatment-Effect Estimation—0
End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling—0
Enhancing Airline Customer Satisfaction: A Machine Learning and Causal Analysis Approach—0
Ensemble Learning with Statistical and Structural Models—0
Entropic Causal Inference for Neurological Applications—0
Entropic Causal Inference: Identifiability and Finite Sample Results—0
Applications of Common Entropy for Causal Inference—0
Entropy and the Kullback-Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation—0
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach—0
Epistemic Limits of Empirical Finance: Causal Reductionism and Self-Reference—0
Error Causal inference for Multi-Fusion models—0
Establishing Causal Relationship Between Whole Slide Image Predictions and Diagnostic Evidence Subregions in Deep Learning—0
Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach—0
Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs—0
Estimating Causal Effects of Text Interventions Leveraging LLMs—0
Estimating causal effects with optimization-based methods: A review and empirical comparison—0
Estimating Causal Effects With Partial Covariates For Clinical Interpretability—0
Estimating Conditional Average Treatment Effects via Sufficient Representation Learning—0
Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interference—0
Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms—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