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 426–450 of 1722 papers

TitleStatusHype
Predictive Performance Comparison of Decision Policies Under ConfoundingCode0
Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets—0
C-XGBoost: A tree boosting model for causal effect estimationCode1
Causal Inference for Human-Language Model CollaborationCode0
De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts—0
On Causal Inference with Model-Based OutcomesCode0
Causal-StoNet: Causal Inference for High-Dimensional Complex DataCode0
Semi-Supervised Learning for Deep Causal Generative ModelsCode1
Can Large Language Models (or Humans) Disentangle Text?Code0
Double Cross-fit Doubly Robust Estimators: Beyond Series RegressionCode0
COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty—0
A Transfer Learning Causal Approach to Evaluate Racial/Ethnic and Geographic Variation in Outcomes Following Congenital Heart Surgery—0
Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality AssessmentCode2
Research on Personal Credit Risk Assessment Methods Based on Causal Inference—0
Limits of Approximating the Median Treatment Effect—0
Lifted Causal Inference in Relational DomainsCode0
Algorithmic syntactic causal identification—0
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey—0
Imputation of Counterfactual Outcomes when the Errors are Predictable—0
Probabilistic Easy Variational Causal Effect—0
CausalCellSegmenter: Causal Inference inspired Diversified Aggregation Convolution for Pathology Image Segmentation—0
Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media—0
Causal Disentanglement for Regulating Social Influence Bias in Social Recommendation—0
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUsCode0
Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door AdjustmentCode0
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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