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

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Showing 1551–1575 of 1722 papers

TitleStatusHype
Computer-Assisted Text Analysis for Social Science: Topic Models and Beyond—0
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations—0
Conceptualizing Treatment Leakage in Text-based Causal Inference—0
Conceptualizing Treatment Leakage in Text-based Causal Inference—0
Conditional Instrumental Variable Regression with Representation Learning for Causal Inference—0
Conformal Counterfactual Inference under Hidden Confounding—0
Confounder Analysis in Measuring Representation in Product Funnels—0
Confounding-Robust Policy Improvement—0
Confounding-Robust Policy Improvement with Human-AI Teams—0
Confronting Machine Learning With Financial Research—0
Connecting Data to Mechanisms with Meta Structual Causal Model—0
Connecting the Dots: Identifying Network Structure via Graph Signal Processing—0
Consistency of Causal Inference under the Additive Noise Model—0
Consistent Causal Inference of Group Effects in Non-Targeted Trials with Finitely Many Effect Levels—0
C-Learner: Constrained Learning for Causal Inference and Semiparametric Statistics—0
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles—0
Contagion Effect Estimation Using Proximal Embeddings—0
Conterfactual Generative Zero-Shot Semantic Segmentation—0
Context-dependent Causality (the Non-Nonotonic Case)—0
Context-Specific Causal Discovery for Categorical Data Using Staged Trees—0
Contextual Multi-Armed Bandits for Causal Marketing—0
Continual Causal Inference with Incremental Observational Data—0
Continual Causal Effect Estimation: Challenges and Opportunities—0
Continuous Treatment Effects with Surrogate Outcomes—0
Contrastive Fairness in Machine Learning—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