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 1701–1722 of 1722 papers

TitleStatusHype
Estimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory—0
Estimating Heterogenous Treatment Effects for Survival Data with Doubly Doubly Robust Estimator—0
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning—0
Estimating Online Influence Needs Causal Modeling! Counterfactual Analysis of Social Media Engagement—0
Estimating Potential Outcome Distributions with Collaborating Causal Networks—0
Estimating the causal effect of an intervention in a time series setting: the C-ARIMA approach—0
Estimating the treatment effect over time under general interference through deep learner integrated TMLE—0
Estimating Treatment Effects in Continuous Time with Hidden Confounders—0
Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders—0
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation—0
Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index—0
Estimating Treatment Effects via Orthogonal Regularization—0
Estimation Considerations in Contextual Bandits—0
Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes—0
Estimation of Treatment Effects in Extreme and Unobserved Data—0
Validating Causal Inference Methods—0
Evaluating Digital Agriculture Recommendations with Causal Inference—0
Evaluating Digital Tools for Sustainable Agriculture using Causal Inference—0
Evaluating Fairness Metrics in the Presence of Dataset Bias—0
Evaluating Interventional Reasoning Capabilities of Large Language Models—0
Assessing the Causal Impact of Humanitarian Aid on Food Security—0
Evaluation Methods and Measures for Causal Learning Algorithms—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