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 1576–1600 of 1722 papers

TitleStatusHype
Confounding-Robust Policy Improvement—0
Counterfactual Mean EmbeddingsCode0
Super learning in the SAS system—0
Multiple Causal Inference with Latent Confounding—0
Consistent Estimation of Propensity Score Functions with Oversampled Exposed SubjectsCode0
The Blessings of Multiple CausesCode0
Machine Learning for Public Administration Research, with Application to Organizational Reputation—0
Bivariate Causal Discovery and its Applications to Gene Expression and Imaging Data Analysis—0
Causal programming: inference with structural causal models as finding instances of a relation—0
Data science is science's second chance to get causal inference right: A classification of data science tasks—0
Causal Inference via Kernel Deviance Measures—0
AFA-PredNet: The action modulation within predictive coding—0
Computer-Assisted Text Analysis for Social Science: Topic Models and Beyond—0
Causal Inference for Survival Analysis—0
Causal Inference on Discrete Data via Estimating Distance Correlations—0
Adversarial Generalized Method of MomentsCode0
Deep Learning for Causal Inference—0
A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection—0
Learning Weighted Representations for Generalization Across Designs—0
Analysis of cause-effect inference by comparing regression errors—0
Closing the loop on multisensory interactions: A neural architecture for multisensory causal inference and recalibration—0
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training—0
Benchmarking Framework for Performance-Evaluation of Causal Inference AnalysisCode0
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees—0
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal DiscoveryCode0
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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