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 1051–1075 of 1722 papers

TitleStatusHype
Determining the Relevance of Features for Deep Neural Networks—0
Developing a general-purpose clinical language inference model from a large corpus of clinical notes—0
Difference-in-Differences Meets Synthetic Control: Doubly Robust Identification and Estimation—0
Difference-in-Differences with Sample Selection—0
Differentially Private Covariate Balancing Causal Inference—0
Differentially Private Joint Independence Test—0
Differentially Private Multi-Site Treatment Effect Estimation—0
Differentially Private Synthetic Control—0
Directed Acyclic Graph Convolutional Networks—0
Discovering Context Specific Causal Relationships—0
Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference—0
Disentangled Representation Learning for Causal Inference with Instruments—0
Distinguishing Cause from Effect Based on Exogeneity—0
Distinguishing cause from effect using observational data: methods and benchmarks—0
Distributionally Robust Causal Inference with Observational Data—0
Robust Multi-instance Learning with Stable Instances—0
Distributional synthetic controls—0
Omitted Labels Induce Nontransitive Paradoxes in Causality—0
Optimal Nonparametric Inference with Two-Scale Distributional Nearest Neighbors—0
Does Financial Literacy Impact Investment Participation and Retirement Planning in Japan?—0
Do LLMs Act as Repositories of Causal Knowledge?—0
Domain Adaptable Prescriptive AI Agent for Enterprise—0
Don't (fully) exclude me, it's not necessary! Causal inference with semi-IVs—0
Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data—0
Double Machine Learning for Conditional Moment Restrictions: IV Regression, Proximal Causal Learning and Beyond—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