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 1251–1275 of 1722 papers

TitleStatusHype
Inferring Heterogeneous Treatment Effects of Crashes on Highway Traffic: A Doubly Robust Causal Machine Learning Approach—0
Inferring Individual Level Causal Models from Graph-based Relational Time Series—0
Inferring physical laws by artificial intelligence based causal models—0
Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities—0
InfoFlowNet: A Multi-head Attention-based Self-supervised Learning Model with Surrogate Approach for Uncovering Brain Effective Connectivity—0
Info Intervention—0
Information Flow Rate for Cross-Correlated Stochastic Processes—0
Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders—0
Instrumented Common Confounding—0
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments—0
Integrating Fuzzy Logic with Causal Inference: Enhancing the Pearl and Neyman-Rubin Methodologies—0
Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation—0
Intelligent Credit Limit Management in Consumer Loans Based on Causal Inference—0
Intelligent Request Strategy Design in Recommender System—0
Interaction Information for Causal Inference: The Case of Directed Triangle—0
International Trade and Intellectual Property—0
Interpretable Gait Recognition by Granger Causality—0
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges—0
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges—0
Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach—0
Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction—0
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems—0
Intervention Generalization: A View from Factor Graph Models—0
Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment—0
Invariant Representations for Reinforcement Learning without Reconstruction—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