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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 1026–1050 of 1722 papers

TitleStatusHype
SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models—0
Symbiotic bacterial network structure involved in carbon and nitrogen metabolism of wood-utilizing insect larvae—0
Asymptotically Unbiased Synthetic Control Methods by Density Matching—0
Synthetic Potential Outcomes and Causal Mixture Identifiability—0
Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset—0
T2TD: Text-3D Generation Model based on Prior Knowledge Guidance—0
Targeted Data Fusion for Causal Survival Analysis Under Distribution Shift—0
Targeted VAE: Structured Inference and Targeted Learning for Causal Parameter Estimation—0
Targeting Learning: Robust Statistics for Reproducible Research—0
Task-specific experimental design for treatment effect estimation—0
TC-LLaVA: Rethinking the Transfer from Image to Video Understanding with Temporal Considerations—0
T-CPDL: A Temporal Causal Probabilistic Description Logic for Developing Logic-RAG Agent—0
Incorporating Causal Effects into Deep Learning Predictions on EHR Data—0
Telling cause from effect in deterministic linear dynamical systems—0
Temporal Inference with Finite Factored Sets—0
Testability of instrument validity under continuous endogenous variables—0
Testing Generalizability in Causal Inference—0
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates—0
Text-driven Video Prediction—0
The Adaptive Doubly Robust Estimator and a Paradox Concerning Logging Policy—0
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data—0
The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective—0
The Counterfactual χ-GAN—0
The Crossover Process: Learnability and Data Protection from Inference Attacks—0
The Deconfounded Recommender: A Causal Inference Approach to Recommendation—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