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 1226–1250 of 1722 papers

TitleStatusHype
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference—0
Identifying and Estimating Causal Effects under Weak Overlap by Generative Prognostic Model—0
Identifying Assumptions and Research Dynamics—0
Identifying Candidate Risk Factors for Prescription Drug Side Effects using Causal Contrast Set Mining—0
Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community—0
Identifying Causal Relations Using Parallel Wikipedia Articles—0
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach—0
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders—0
Identifying Macro Causal Effects in C-DMGs—0
Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confounding—0
Causal Inference Despite Limited Global Confounding via Mixture Models—0
Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model—0
Intact-VAE: Estimating Treatment Effects under Unobserved Confounding—0
Impact of Physical Activity on Quality of Life During Pregnancy: A Causal ML Approach—0
Causal Discovery with a Mixture of DAGs—0
Out-of-distribution robustness for multivariate analysis via causal regularisation—0
Improving Open-Domain Dialogue Evaluation with a Causal Inference Model—0
Imputation of Counterfactual Outcomes when the Errors are Predictable—0
Individual Causal Inference Using Panel Data With Multiple Outcomes—0
Individualized Decision-Making Under Partial Identification: Three Perspectives, Two Optimality Results, and One Paradox—0
Inference for max-linear Bayesian networks with noise—0
Inference with few treated units—0
Inferring causal structure: a quantum advantage—0
Inferring deterministic causal relations—0
Inferring dynamic regulatory interaction graphs from time series data with perturbations—0
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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