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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 351–375 of 1722 papers

TitleStatusHype
Assimilative Causal Inference—0
Causal inference approach to appraise long-term effects of maintenance policy on functional performance of asphalt pavements—0
Causal Forecasting for Pricing—0
Causal Inference Based Single-branch Ensemble Trees For Uplift Modeling—0
Causal Post-Processing of Predictive Models—0
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components—0
Causal Inference by Stochastic Complexity—0
Causal Inference by String Diagram Surgery—0
Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions—0
Causal Inference (C-inf) -- closed form worst case typical phase transitions—0
A fast PC algorithm for high dimensional causal discovery with multi-core PCs—0
Causal Feature Selection with Dimension Reduction for Interpretable Text Classification—0
Causal inference for climate change events from satellite image time series using computer vision and deep learning—0
Causal inference for data-driven debugging and decision making in cloud computing—0
Causal Inference for Early Detection of Pathogenic Social Media Accounts—0
Causal Inference for Event Pairs in Multivariate Point Processes—0
Causal Inference for Experiments with Latent Outcomes: Key Results and Their Implications for Design and Analysis—0
Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes—0
Artificial Intelligence and Aesthetic Judgment—0
A Compositional Atlas for Algebraic Circuits—0
Causal Inference Struggles with Agency on Online Platforms—0
Causal Inference for Recommendation: Foundations, Methods and Applications—0
A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery—0
Causal Inference for Survival Analysis—0
Accurate Use of Label Dependency in Multi-Label Text Classification Through the Lens of Causality—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