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 376–400 of 1722 papers

TitleStatusHype
Causal Inference for Time series Analysis: Problems, Methods and Evaluation—0
Causal Inference from Slowly Varying Nonstationary Processes—0
Causal Inference from Small High-dimensional Datasets—0
A scoping review of causal methods enabling predictions under hypothetical interventions—0
A General Causal Inference Framework for Cross-Sectional Observational Data—0
Causal Inference in Disease Spread across a Heterogeneous Social System—0
Causal inference in drug discovery and development—0
A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery—0
Causal Inference in Finance: An Expertise-Driven Model for Instrument Variables Identification and Interpretation—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
Causal Inference in Geosciences with Kernel Sensitivity Maps—0
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond—0
Causal Inference in Network Economics—0
Causal inference in network experiments: regression-based analysis and design-based properties—0
Accurate Use of Label Dependency in Multi-Label Text Classification Through the Lens of Causality—0
Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality—0
Causal Inference in Observational Data—0
Attribution Projection Calculus: A Novel Framework for Causal Inference in Bayesian Networks—0
Causal Inference with Complex Treatments: A Survey—0
Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder—0
Causal Inference in the Presence of Interference in Sponsored Search Advertising—0
Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts—0
Causal Inference Isn't Special: Why It's Just Another Prediction Problem—0
Causal Inference with Deep Causal Graphs—0
Show:102550
← PrevPage 16 of 69Next →
All datasetsIHDPJobsIDHP

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