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 1526–1550 of 1722 papers

TitleStatusHype
Analyzing User Characteristics of Hate Speech Spreaders on Social Media—0
Causal versions of Maximum Entropy and Principle of Insufficient Reason—0
Cause-Effect Deep Information Bottleneck For Systematically Missing Covariates—0
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations—0
Causes of evolutionary divergence in prostate cancer—0
CauSkelNet: Causal Representation Learning for Human Behaviour Analysis—0
CAVIAR: Categorical-Variable Embeddings for Accurate and Robust Inference—0
CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems—0
Chemical Property Prediction Under Experimental Biases—0
CIER: A Novel Experience Replay Approach with Causal Inference in Deep Reinforcement Learning—0
Classifying Treatment Responders Under Causal Effect Monotonicity—0
Closing the loop on multisensory interactions: A neural architecture for multisensory causal inference and recalibration—0
ClusterSC: Advancing Synthetic Control with Donor Selection—0
Coarsened confounding for causal effects: a large-sample framework—0
Collaborative causal inference on distributed data—0
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data—0
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis—0
Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects—0
Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decision—0
Comment on "Blessings of Multiple Causes"—0
Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions—0
Complementary Advantages of ChatGPTs and Human Readers in Reasoning: Evidence from English Text Reading Comprehension—0
Compositional Models for Estimating Causal Effects—0
Computational Causal Inference—0
Computational identification of ketone metabolism as a key regulator of sleep stability and circadian dynamics via real-time metabolic profiling—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