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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 751–775 of 1722 papers

TitleStatusHype
Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decision—0
A novel approach to the relationships between data features -- based on comprehensive examination of mathematical, technological, and causal methodology—0
Causal Inference for Time series Analysis: Problems, Methods and Evaluation—0
A Causal Lens for Controllable Text Generation—0
Applications of Common Entropy for Causal Inference—0
Epistemic Limits of Empirical Finance: Causal Reductionism and Self-Reference—0
DROID: Driver-centric Risk Object Identification—0
DRTCI: Learning Disentangled Representations for Temporal Causal Inference—0
Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects—0
Dynamical causality under invisible confounders—0
CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression—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 discriminative approach for finding and characterizing positivity violations using decision trees—0
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis—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
Categoroids: Universal Conditional Independence—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