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
A Survey of Online Hate Speech through the Causal Lens—0
A Survey on Causal Discovery: Theory and Practice—0
Asymptotic Causal Inference—0
Asymptotic Properties of the Distributional Synthetic Controls—0
A Synthetic Business Cycle Approach to Counterfactual Analysis with Nonstationary Macroeconomic Data—0
A scoping review of causal methods enabling predictions under hypothetical interventions—0
A Systems Thinking Approach to Algorithmic Fairness—0
INTAGS: Interactive Agent-Guided Simulation—0
A Topological Perspective on Causal Inference—0
A Transfer Learning Causal Approach to Evaluate Racial/Ethnic and Geographic Variation in Outcomes Following Congenital Heart Surgery—0
Attributes for Causal Inference in Longitudinal Observational Databases—0
Attribution Projection Calculus: A Novel Framework for Causal Inference in Bayesian Networks—0
A Tutorial on Doubly Robust Learning for Causal Inference—0
A Two-Stage Interpretable Matching Framework for Causal Inference—0
Auction Throttling and Causal Inference of Online Advertising Effects—0
Auditing Search Engines for Differential Satisfaction Across Demographics—0
A unified theory of information transfer and causal relation—0
A unifying approach for doubly-robust _1 regularized estimation of causal contrasts—0
Causal Explanations for Sequential Decision Making Under Uncertainty—0
A Unifying Framework for Robust and Efficient Inference with Unstructured Data—0
Automated hypothesis generation via Evolutionary Abduction—0
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition—0
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands—0
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—0
Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphs—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