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 1276–1300 of 1722 papers

TitleStatusHype
Inverting estimating equations for causal inference on quantiles—0
Investigating potential causes of Sepsis with Bayesian network structure learning—0
Investigating the Relationship Between Physical Activity and Tailored Behavior Change Messaging: Connecting Contextual Bandit with Large Language Models—0
It’s quality and quantity: the effect of the amount of comments on online suicidal posts—0
Ivy: Instrumental Variable Synthesis for Causal Inference—0
Joint Causal Inference from Multiple Contexts—0
Joint Value Estimation and Bidding in Repeated First-Price Auctions—0
Justifying Information-Geometric Causal Inference—0
KANITE: Kolmogorov-Arnold Networks for ITE estimation—0
Kernel-based Approach to Handle Mixed Data for Inferring Causal Graphs—0
K-Fold Causal BART for CATE Estimation—0
Kolmogorov-Arnold Networks for Time Series Granger Causality Inference—0
Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference—0
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey—0
Large Language Models as Co-Pilots for Causal Inference in Medical Studies—0
Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data—0
Latent Convergent Cross Mapping—0
Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism—0
Layout-Based Causal Inference for Object Navigation—0
Learned Causal Method Prediction—0
Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering—0
Learning Causal Effects via Weighted Empirical Risk Minimization—0
Learning Causal Overhypotheses through Exploration in Children and Computational Models—0
Learning Causal Models from Conditional Moment Restrictions by Importance Weighting—0
Learning Causal Relationships from Conditional Moment Restrictions by Importance Weighting—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