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 Analysis and Classification of Traffic Crash Injury Severity Using Machine Learning Algorithms—0
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks—0
Causal Inference for Time series Analysis: Problems, Methods and Evaluation—0
Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions—0
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference—0
Comment on "Blessings of Multiple Causes"—0
DROID: Driver-centric Risk Object Identification—0
DRTCI: Learning Disentangled Representations for Temporal Causal Inference—0
Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decision—0
Dynamical causality under invisible confounders—0
A novel approach to the relationships between data features -- based on comprehensive examination of mathematical, technological, and causal methodology—0
A General Causal Inference Framework for Cross-Sectional Observational Data—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
Causal inference in drug discovery and development—0
Dynamic Survival Transformers for Causal Inference with Electronic Health Records—0
A Causal Lens for Controllable Text Generation—0
Estimating Potential Outcome Distributions with Collaborating Causal Networks—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
Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems—0
Causal Inference in Geoscience and Remote Sensing from Observational Data—0
A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings—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
Estimating the treatment effect over time under general interference through deep learner integrated TMLE—0
Causal inference in network experiments: regression-based analysis and design-based properties—0
Embodied Scene-aware Human Pose Estimation—0
Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects—0
Emoticons vs. Emojis on Twitter: A Causal Inference Approach—0
CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression—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
A discriminative approach for finding and characterizing positivity violations using decision trees—0
Enhancing Airline Customer Satisfaction: A Machine Learning and Causal Analysis Approach—0
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis—0
Categoroids: Universal Conditional Independence—0
Causal Inference in the Presence of Interference in Sponsored Search Advertising—0
A Two-Stage Interpretable Matching Framework for Causal Inference—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
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data—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