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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 1126–1150 of 1722 papers

TitleStatusHype
Valid Inference After Causal Discovery—0
Variable Selection for Causal Inference via Outcome-Adaptive Random Forest—0
Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison—0
Variance Reduction in Bipartite Experiments through Correlation Clustering—0
Variational Auto-Encoder Architectures that Excel at Causal Inference—0
Variational Causal Autoencoder for Interventional and Counterfactual Queries—0
Variational Deterministic Uncertainty Quantification—0
Virtual Control Group: Measuring Hidden Performance Metrics—0
Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis—0
VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference—0
Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models—0
Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference—0
On L_2-consistency of nearest neighbor matching—0
Weak Instrumental Variables: Limitations of Traditional 2SLS and Exploring Alternative Instrumental Variable Estimators—0
What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems—0
What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness—0
Whole Brain Network Dynamics of Epileptic Seizures at Single Cell Resolution—0
Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference—0
The Cost of Influence: How Gifts to Physicians Shape Prescriptions and Drug Costs—0
Effects of Epileptiform Activity on Discharge Outcome in Critically Ill Patients—0
Wildfire smoke plume segmentation using geostationary satellite imagery—0
Woolf et als GWAS by subtraction is not useful for cross-generational Mendelian randomization studies—0
World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Child—0
XLTime: A Cross-Lingual Knowledge Transfer Framework for Zero-Shot Low-Resource Language Temporal Expression Extraction—0
XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction—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