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 751–800 of 1722 papers

TitleStatusHype
Causal Inference in Network Economics—0
A Transfer Learning Causal Approach to Evaluate Racial/Ethnic and Geographic Variation in Outcomes Following Congenital Heart Surgery—0
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond—0
Causal Inference in Geosciences with Kernel Sensitivity Maps—0
A Topological Perspective on Causal Inference—0
A Graphical Approach to State Variable Selection in Off-policy Learning—0
Causal Inference in Geoscience and Remote Sensing from Observational Data—0
Effect of secular trend in drug effectiveness study in real world data—0
Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems—0
Causal Inference in Finance: An Expertise-Driven Model for Instrument Variables Identification and Interpretation—0
Eco-efficiency as a Catalyst for Citizen Co-production: Evidence from Chinese Cities—0
Early Identification of Pathogenic Social Media Accounts—0
Causal Inference in Educational Systems: A Graphical Modeling Approach—0
INTAGS: Interactive Agent-Guided Simulation—0
A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings—0
A continuous Structural Intervention Distance to compare Causal Graphs—0
A Causal Adjustment Module for Debiasing Scene Graph Generation—0
A Fast Kernel-based Conditional Independence test with Application to Causal Discovery—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
A Multi-class Ride-hailing Service Subsidy System Utilizing Deep Causal Networks—0
Dynamic Survival Transformers for Causal Inference with Electronic Health Records—0
Causal inference in drug discovery and development—0
DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models—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
Dynamic Regularized CBDT: Variance-Calibrated Causal Boosting for Interpretable Heterogeneous Treatment Effects—0
Causal Inference in Disease Spread across a Heterogeneous Social System—0
Embodied Scene-aware Human Pose Estimation—0
A Systems Thinking Approach to Algorithmic Fairness—0
Emoticons vs. Emojis on Twitter: A Causal Inference Approach—0
Dynamical causality under invisible confounders—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
A scoping review of causal methods enabling predictions under hypothetical interventions—0
End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling—0
DRTCI: Learning Disentangled Representations for Temporal Causal Inference—0
DROID: Driver-centric Risk Object Identification—0
Causal Inference from Small High-dimensional Datasets—0
Causal Inference from Slowly Varying Nonstationary Processes—0
A Synthetic Business Cycle Approach to Counterfactual Analysis with Nonstationary Macroeconomic Data—0
Causal Inference for Time series Analysis: Problems, Methods and Evaluation—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
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks—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