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 1251–1300 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
Inverting estimating equations for causal inference on quantiles—0
Investigating potential causes of Sepsis with Bayesian network structure learning—0
Investigating the Relationship Between Physical Activity and Tailored Behavior Change Messaging: Connecting Contextual Bandit with Large Language Models—0
It’s quality and quantity: the effect of the amount of comments on online suicidal posts—0
Ivy: Instrumental Variable Synthesis for Causal Inference—0
Joint Causal Inference from Multiple Contexts—0
Joint Value Estimation and Bidding in Repeated First-Price Auctions—0
Justifying Information-Geometric Causal Inference—0
KANITE: Kolmogorov-Arnold Networks for ITE estimation—0
Kernel-based Approach to Handle Mixed Data for Inferring Causal Graphs—0
K-Fold Causal BART for CATE Estimation—0
Kolmogorov-Arnold Networks for Time Series Granger Causality Inference—0
Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference—0
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey—0
Large Language Models as Co-Pilots for Causal Inference in Medical Studies—0
Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data—0
Latent Convergent Cross Mapping—0
Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism—0
Layout-Based Causal Inference for Object Navigation—0
Learned Causal Method Prediction—0
Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering—0
Learning Causal Effects via Weighted Empirical Risk Minimization—0
Learning Causal Overhypotheses through Exploration in Children and Computational Models—0
Learning Causal Models from Conditional Moment Restrictions by Importance Weighting—0
Learning Causal Relationships from Conditional Moment Restrictions by Importance Weighting—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