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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 476–500 of 1722 papers

TitleStatusHype
Robust agents learn causal world models—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
Conditional Generative Models are Sufficient to Sample from Any Causal Effect EstimandCode0
Navigating Market Turbulence: Insights from Causal Network Contagion Value at Risk—0
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study—0
A flexible Bayesian g-formula for causal survival analyses with time-dependent confoundingCode0
PresAIse, A Prescriptive AI Solution for Enterprises—0
Integrating Large Language Models in Causal Discovery: A Statistical Causal ApproachCode0
DoubleMLDeep: Estimation of Causal Effects with Multimodal Data—0
Bayesian Causal Inference with Gaussian Process NetworksCode0
Explaining Text Classifiers with Counterfactual RepresentationsCode0
Continuous Treatment Effects with Surrogate Outcomes—0
How Being Inside or Outside of Buildings Affects the Causal Relationship Between Weather and Pain Among People Living with Chronic Pain—0
Causal Coordinated Concurrent Reinforcement Learning—0
Graph Neural Networks: Theory for Estimation with Application on Network Heterogeneity—0
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach—0
Position: AI/ML Influencers Have a Place in the Academic Process—0
SLANG: New Concept Comprehension of Large Language ModelsCode1
Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach—0
Deep Learning-based Group Causal Inference in Multivariate Time-seriesCode0
Modeling Latent Selection with Structural Causal Models—0
Deep Learning With DAGs—0
Valid causal inference with unobserved confounding in high-dimensional settingsCode0
Proximal Causal Inference With Text DataCode0
Information Flow Rate for Cross-Correlated Stochastic Processes—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